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You literally got rejected and you said
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no. Look at it again. Last month, a
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student group that I used to be a part
[00:08] (8.00s)
of at UCLA reached out asking if I
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wanted to give a talk. I've had five
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interns at Meta. When I think about the
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ones that were rock stars, they had
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the Hey everyone, welcome back to the
[00:20] (20.48s)
channel. My name is Ryan and if you're
[00:22] (22.24s)
new here, I try to share conversations
[00:24] (24.48s)
on this channel with more senior
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software engineers so that you can learn
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from their stories and their experience.
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There is so much that no one told me in
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college that I wanted to share with
[00:35] (35.12s)
these students. And so I enlisted some
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help from my friend Ricky. You know, my
[00:38] (38.80s)
way is usually right and I make a bunch
[00:40] (40.16s)
of money and they're like, "Okay,
[00:41] (41.20s)
actually." We hosted a Q&A style event
[00:43] (43.44s)
and we received hundreds of questions,
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some of which I really didn't expect to
[00:48] (48.32s)
see. How do you make as much money as
[00:50] (50.64s)
possible? I would say and others I
[00:52] (52.96s)
expected to see, but hopefully our
[00:55] (55.04s)
personal stories were helpful. So, I
[00:57] (57.36s)
feel like Ryan scammed me because I got
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really lucky and I got all the offers at
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every place I interviewed at except for
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one. Like my mom, she was my hater. She
[01:07] (67.20s)
believed I wouldn't succeed without an
[01:08] (68.84s)
MBA. Here is the full
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video. Excited to be here, everyone. My
[01:15] (75.44s)
name's Ryan. When Ashley reached out, I
[01:17] (77.76s)
was excited to talk to you all because I
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feel like there's so many things that we
[01:21] (81.92s)
learned in industry that no one tells
[01:24] (84.80s)
you until you actually fail and you get
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into industry. And I'm hoping that we
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can tell you a lot of that today. Um, do
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you want to introduce? Yeah. Hey, I'm
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Ricky. Uh, really excited to be here,
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too. I feel like um, so much of my
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success postgrad is from everything that
[01:41] (101.12s)
I learned at UCLA and all the people
[01:42] (102.64s)
that I've met. So really excited to be
[01:44] (104.80s)
here and kind of give back. Before we
[01:46] (106.72s)
get started, when I right before I got
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into industry, I knew nothing about the
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levels. And so like what even is staff?
[01:53] (113.52s)
Like when I got promoted to staff, my
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parents thought it was lower than
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senior. So u it doesn't sound that good.
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So this is just to give you a little
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idea. This is like levels.fy. Of course,
[02:04] (124.80s)
when you get promoted, you make more
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money, but also I feel like your work
[02:09] (129.44s)
becomes a lot more interesting because
[02:11] (131.04s)
it becomes higher level, more impactful,
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and it's a lot more satisfying. I'm just
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showing this to you so that when we say
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junior, mid-level, senior staff in this
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talk, you know, um what it means and
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like what it refers to. So, I'll give
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you a little bit of a explanation of
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what each level means, right? Because
[02:28] (148.80s)
you look at it and you're like, you
[02:30] (150.16s)
know, what does it actually mean? So at
[02:32] (152.16s)
the junior level, so this is usually
[02:34] (154.24s)
where new grads are right here. The
[02:36] (156.80s)
expectation is just for you to be able
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to kind of code and um you know push out
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some stuff with a decent amount of help.
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Whereas once you get to the mid level,
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there's an expectation that yes, now you
[02:50] (170.56s)
can kind of do more do more things on
[02:52] (172.32s)
your own, but you still need help here
[02:53] (173.76s)
and there. And usually from junior to
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midle I think on average it takes people
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maybe two to 3 to four years and then
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from mid-level to senior at the senior
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level. So we say that senior level is
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terminal because a lot of people will
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get to senior and just stop. So there's
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usually no expectation to go beyond
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senior. It's more like do you want to
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but there is an expectation for you to
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go from junior to mid-level to senior.
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So mid-level to senior takes around
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maybe also like 2 3 4 years. At the
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senior level, the expectation is that
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someone will give you a tax and you can
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kind of finish it on your own and you
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don't need that much help. And then at
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the staff level, so again, this is if
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you actually want to get to that level,
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it's a lot more responsibilities. Some
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people say that, you know, it's not even
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worth the pay increase because you
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probably uh have to work a lot more in
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terms of your, you know, your hourly
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wage, right? Like how much you make per
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hour. Uh but at this level the
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expectation is that you're setting the
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strategy for the team. You're really
[03:52] (232.48s)
thinking about the direction that the
[03:54] (234.80s)
team should go in and you're kind of
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leading all the you know senior
[03:57] (237.52s)
mid-level junior folks. So hopefully
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that was helpful. Um I thought you know
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I had no clue what all of these things
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were when I first started at Google. So
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you know hopefully this gives you a
[04:09] (249.92s)
better view of what all the levels mean.
[04:11] (251.60s)
Uh I was wondering is it true at Google
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that terminal level is actually L4? cuz
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I've heard that somewhere. I wasn't uh
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with time not really. Um so there
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definitely is more and more of an
[04:24] (264.56s)
expectation that you can eventually get
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this senior but I think other companies
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um are more aggressive. So up other
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companies often have a policy called up
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or out. So if you aren't able to get to
[04:37] (277.44s)
that level then bye-bye. Um
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but but I think at Google at least it's
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a little bit more relaxed and it's not
[04:46] (286.80s)
as aggressive of a policy. Yeah. Yeah.
[04:49] (289.52s)
And the up and out policies by the way
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they're not meant to be intense and
[04:53] (293.68s)
scary like 90 I don't have the exact you
[04:55] (295.92s)
know data but it's like 90 plus%
[04:58] (298.00s)
probably um you know make it to those
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levels. So it's actually kind of like uh
[05:03] (303.36s)
I guess an upper bound of how quickly
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you'll grow. Go ahead, Bill. What's
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what's the mix of uh of technical and
[05:10] (310.16s)
supervisory responsibilities that you
[05:12] (312.32s)
have as you're going up those levels? I
[05:14] (314.48s)
see. I mean, on a high level, like
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junior, people are handing you tasks,
[05:18] (318.24s)
you're doing them with a lot of help.
[05:19] (319.76s)
Mid level, you're not being watched on
[05:22] (322.80s)
how you do it, but people are still
[05:24] (324.64s)
giving you things to do. Senior people
[05:27] (327.76s)
tell you there's an area that needs your
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help, and you go and create the tasks,
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and you do them. and staff is you find
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the areas that need things to be done.
[05:37] (337.52s)
That's like the high level. How accurate
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is levels? FYI, sorry during this. It's
[05:41] (341.92s)
pretty accurate except the fluctuations
[05:45] (345.52s)
in the equity. That's actually Yeah. So,
[05:47] (347.52s)
I just pulled these numbers today. The
[05:49] (349.36s)
Meta's numbers on the left, like why are
[05:51] (351.68s)
they so much higher than Google's? Well,
[05:53] (353.60s)
Meta stock has been like ripping
[05:55] (355.68s)
recently, so like like that's why. But
[05:58] (358.80s)
generally, it it's pretty it's pretty
[06:00] (360.64s)
accurate. So yeah, let's get into some
[06:02] (362.24s)
of the prepared questions. Before we got
[06:03] (363.76s)
here today, you know, in the RSVP,
[06:05] (365.84s)
there's the ability for you to submit
[06:07] (367.84s)
questions. We'll go through those and
[06:09] (369.76s)
we'll kind of answer with various
[06:11] (371.12s)
stories. Maybe Ashley, you can MC. A lot
[06:13] (373.20s)
of people in the audience have
[06:14] (374.72s)
internship plans and we're all wondering
[06:16] (376.88s)
how do we succeed as an intern. The
[06:18] (378.88s)
rubric at different companies, it
[06:20] (380.96s)
fluctuates slightly, but it's somewhat
[06:23] (383.20s)
similar. When you get into that
[06:25] (385.52s)
internship, they're going to give you a
[06:27] (387.20s)
project and they're going to see how
[06:30] (390.40s)
quickly you can get the project done. If
[06:32] (392.40s)
you can do that project in half of your
[06:34] (394.80s)
internship's time, obviously that's
[06:36] (396.88s)
going to be really good. So, they're
[06:38] (398.08s)
going to be measuring your productivity.
[06:40] (400.08s)
That's a big part of it. Another thing
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is the quality of your work. And you can
[06:44] (404.08s)
measure that by how many times your code
[06:47] (407.52s)
needs to be reviewed until it's
[06:49] (409.52s)
submittable. So at the beginning of the
[06:51] (411.84s)
internship you write code and then it's
[06:54] (414.88s)
trash and it needs to be reviewed many
[06:57] (417.36s)
many times because you don't know the
[06:58] (418.80s)
code base yet. But ideally by the end of
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the internship your code should be
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reviewed with nits and landing with one
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or zero revisions. So that should be
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changing. And then lastly you should be
[07:11] (431.60s)
communicating and um you know pushing
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like after you finish that project what
[07:15] (435.76s)
is that thing that you do afterwards? if
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you propose improvements on top of your
[07:19] (439.84s)
project, maybe you propose a new project
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entirely like that that's like a really
[07:24] (444.80s)
strong internship performance. And so
[07:27] (447.44s)
that was that was a lot. That's pretty
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intense, but that's what it looks like
[07:31] (451.20s)
if you want to really crush it and for
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sure get I would say I definitely agree
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like most of the time internship
[07:37] (457.68s)
projects should be pretty well scoped
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like there's kind of a defined end. So
[07:42] (462.00s)
that's not usually true more at the
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higher levels when you know when you're
[07:45] (465.68s)
a full-time employee. the problems are a
[07:48] (468.00s)
little bit more ambiguous, the solutions
[07:49] (469.60s)
a little bit more ambiguous, but usually
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as an intern, they're I feel like
[07:52] (472.96s)
they're really just trying to see if you
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can like, you know, do some basic
[07:55] (475.84s)
coding, kind of finish a basic task. But
[07:58] (478.16s)
your your project was actually not like
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that, right? Tell that story. Maybe it
[08:02] (482.40s)
can help some people if they Yeah. So
[08:05] (485.12s)
during my junior year um my internship
[08:09] (489.20s)
was at Google and my internship I only
[08:13] (493.28s)
wrote 800 lines of code which is really
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low and I was really stressed out
[08:17] (497.60s)
because I looked at my peers you know my
[08:19] (499.68s)
other UCLA friends that were Google
[08:21] (501.40s)
interns. I was halfway through the
[08:23] (503.36s)
internship and I had submitted like 300
[08:25] (505.68s)
lines of code and I look at my friend
[08:27] (507.68s)
and they've submitted 11k lines of code
[08:29] (509.84s)
and I'm like oh Um but in the end
[08:33] (513.52s)
it was fine and the reason was because
[08:36] (516.24s)
um while lines of code is definitely you
[08:38] (518.72s)
know part of the metrics that people
[08:41] (521.12s)
might use to evaluate your productivity
[08:43] (523.12s)
they also look at other things. So for
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me I really um took a lot of time trying
[08:47] (527.20s)
to understand the codebase. I wrote a
[08:49] (529.12s)
really complex design doc that explored
[08:51] (531.20s)
a lot of pros and cons of different
[08:53] (533.12s)
approaches. So the design itself was
[08:55] (535.44s)
very technical and that's how I was able
[08:57] (537.36s)
to show my productivity and that I was
[08:59] (539.20s)
able to kind of find a solution to this
[09:01] (541.92s)
um problem at work. You know even though
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I at the end of my internship I still
[09:06] (546.00s)
only wrote n 800 900 lines of code I did
[09:09] (549.04s)
get a return offer. It was really nice.
[09:10] (550.64s)
So that's kind of what happened in my
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internship. Next questions after we
[09:15] (555.68s)
hopefully all get our return offers and
[09:17] (557.68s)
we're working full-time. We're all
[09:19] (559.44s)
wondering how do you be successful and
[09:21] (561.36s)
how do we get promoted? So, how did you
[09:23] (563.28s)
guys get promoted so quickly to staff
[09:26] (566.24s)
engineer in your 20s? You know, going
[09:27] (567.92s)
back to the levels that we saw earlier,
[09:29] (569.68s)
right? We had junior, mid-level, senior,
[09:32] (572.56s)
staff. So, at each level, you get to
[09:35] (575.84s)
kind of a steady state where you're able
[09:37] (577.28s)
to do well at that level, right? But if
[09:39] (579.92s)
you want to get to the next level, you
[09:41] (581.44s)
really need to be thinking about what
[09:43] (583.44s)
exactly does a next level project look
[09:45] (585.84s)
like? How do I start acting like the
[09:47] (587.84s)
next level, right? Let's say you were a
[09:50] (590.64s)
junior level and you were super
[09:53] (593.28s)
productive. You did a bunch of junior
[09:55] (595.44s)
level projects. That is not enough to
[09:57] (597.76s)
get to the mid-level because you have
[09:59] (599.12s)
not been able to demonstrate that you
[10:00] (600.64s)
can do a mid-level project. So, what is
[10:02] (602.64s)
actually more impactful is for you to
[10:04] (604.32s)
do, you know, one mid-level project that
[10:06] (606.64s)
solidly solidly shows that you're at
[10:09] (609.28s)
that level. So, I think what Ryan and I
[10:11] (611.92s)
probably did really well is that we were
[10:13] (613.68s)
really able to think about what is the
[10:15] (615.04s)
next level project? How can I do it and
[10:16] (616.80s)
how can I get there? So, we were really
[10:18] (618.40s)
focused on making sure that we weren't
[10:20] (620.24s)
spending too much time on projects that
[10:22] (622.16s)
wouldn't necessarily get us to the next
[10:23] (623.68s)
level. You'll hear this key word, your
[10:25] (625.76s)
manager will probably say it a lot,
[10:27] (627.12s)
which is uh behaviors, which is that the
[10:30] (630.64s)
things that get you promoted is not just
[10:32] (632.80s)
doing a ton of work at your current
[10:34] (634.96s)
level. Like I say, you're junior
[10:36] (636.48s)
engineer, you do 10 times as many
[10:38] (638.88s)
features as a junior engineer. That will
[10:42] (642.00s)
get you a really good rating. Your
[10:43] (643.52s)
performance review will be good. But
[10:45] (645.52s)
when your manager is looking to fill out
[10:47] (647.28s)
like a the rubric for the next level for
[10:49] (649.52s)
instance, none of the things will be
[10:51] (651.60s)
checked off. There's nothing about, you
[10:53] (653.44s)
know, initiative or doing anything
[10:55] (655.84s)
that's expected of the mid level. So
[10:58] (658.64s)
like the thing that gets you promoted
[11:00] (660.40s)
quickly is really understanding the next
[11:03] (663.04s)
level's behaviors and going and taking
[11:05] (665.76s)
initiative and finding projects that fit
[11:07] (667.60s)
that. Um, and so I remember when I was
[11:10] (670.16s)
like, you know, I was really really
[11:12] (672.08s)
eager to get promoted and so I was
[11:13] (673.84s)
constantly talking to my manager like,
[11:15] (675.84s)
you know, as soon as I got promoted to
[11:17] (677.36s)
one level, I was like, "Okay, what's
[11:18] (678.96s)
what's the next level? What what can I
[11:20] (680.56s)
do?" Maybe that was annoying for my
[11:22] (682.24s)
manager, but he was really helpful in
[11:25] (685.52s)
teaching me what were the things that I
[11:27] (687.68s)
needed to pick up so I could continue to
[11:30] (690.00s)
pick up next level opportunities. And I
[11:32] (692.16s)
think that's a big part of why my
[11:34] (694.24s)
promotions were as fast as they were.
[11:36] (696.88s)
Next question. How much of your success
[11:39] (699.60s)
in software engineering and getting
[11:41] (701.28s)
promotions is just luck? Although luck
[11:43] (703.68s)
is a big part of it, you can actually do
[11:46] (706.08s)
things to increase your luck. When I
[11:48] (708.08s)
think back to a lot of the projects and
[11:50] (710.56s)
things that got me promoted, there was
[11:52] (712.48s)
some level of initiative where I went
[11:54] (714.80s)
out of my way to find something. I'll
[11:57] (717.04s)
give you an example like actually, you
[11:58] (718.96s)
know, actually when I graduated UCLA, I
[12:00] (720.88s)
went to Amazon and I was floundering
[12:03] (723.12s)
like I was not doing well at all. But
[12:04] (724.88s)
after I was there for like eight months,
[12:07] (727.52s)
I realized like this is I'm not
[12:09] (729.20s)
learning. I don't know what the growth
[12:10] (730.88s)
to the next level looks like. And so I
[12:13] (733.12s)
just took matters into my own hands and
[12:14] (734.96s)
you know applied to a bunch of different
[12:16] (736.40s)
places. I got really lucky and I got all
[12:18] (738.96s)
the offers at every place I interviewed
[12:20] (740.88s)
at except for one which if you're
[12:22] (742.96s)
curious what that place is that rejected
[12:24] (744.64s)
me, maybe I can tell you off camera
[12:26] (746.96s)
later. But um you know like I ended up
[12:31] (751.36s)
getting to Meta and that kind of like
[12:32] (752.72s)
set up my career for the rest and you
[12:34] (754.88s)
could say that it was lucky that I got
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the interview but also you know I
[12:39] (759.36s)
grinded to like you know apply to all
[12:41] (761.28s)
the places grinded leak code and like
[12:43] (763.76s)
that really increased my luck. A good
[12:45] (765.44s)
part of its luck no doubt but also you
[12:47] (767.68s)
have a lot of uh agency and so I think
[12:50] (770.24s)
that's you know really important too.
[12:52] (772.00s)
Definitely agree. I think um the way I
[12:53] (773.84s)
would frame it is that luck is two parts
[12:55] (775.52s)
right? One is the opportunity coming and
[12:57] (777.60s)
the other part is you being prepared for
[12:59] (779.36s)
that opportunity when it comes because
[13:01] (781.28s)
if you get an interview, right, that
[13:02] (782.88s)
could be luck, right? Maybe you talk to
[13:04] (784.48s)
the right person at the career fair and
[13:06] (786.56s)
they're like, "Oh, you know, why not?"
[13:08] (788.16s)
You know, and they give it to you, but
[13:09] (789.68s)
you're not prepared, right? You're not
[13:11] (791.44s)
like studying and preparing for the
[13:13] (793.12s)
interview and then that opportunity is
[13:14] (794.48s)
going to pass you by. And I feel like
[13:16] (796.24s)
that is really true, you know, not just
[13:18] (798.00s)
when you're finding a job, but also at
[13:19] (799.84s)
work. And in Ryan's case, right, he is
[13:22] (802.72s)
setting himself up for more
[13:24] (804.40s)
opportunities by leaving Amazon. Um, and
[13:27] (807.84s)
of course, like he was prepared um and
[13:30] (810.32s)
ready when those opportunities came to
[13:31] (811.76s)
him. If you had one piece of advice to
[13:34] (814.56s)
give us, what would it be? I love this
[13:37] (817.52s)
question cuz I a lot of people have
[13:40] (820.16s)
asked me this and if if you don't take
[13:43] (823.52s)
anything from today's talk, I hope at
[13:45] (825.84s)
least you take this part. I would say
[13:47] (827.52s)
that the one piece of advice is to ask
[13:49] (829.84s)
tons of questions. Keep asking questions
[13:51] (831.92s)
when you're at work. I mentored a lot of
[13:55] (835.04s)
interns. And one intern mentee I had,
[13:57] (837.76s)
she at the beginning of her internship,
[14:00] (840.08s)
she asked me, you know, Ricky, if you
[14:02] (842.32s)
had one thing, one piece of advice, what
[14:04] (844.00s)
would be? And I told her, ask questions,
[14:05] (845.28s)
keep asking questions, keep asking
[14:06] (846.56s)
questions. And at the end of her
[14:08] (848.64s)
internship, I asked her, okay, so is
[14:10] (850.88s)
there anything you wish I told you at
[14:12] (852.48s)
the beginning of your internship? And
[14:14] (854.00s)
she was like, I wish you told me to ask
[14:15] (855.52s)
questions. And I was like, "No, no, no.
[14:17] (857.12s)
I definitely told you that. I that's I'm
[14:19] (859.60s)
100% sure I told you that." And she told
[14:22] (862.24s)
me, "Yeah, Ricky, you did, but like you
[14:24] (864.16s)
didn't mean it." You know, I really want
[14:26] (866.00s)
to emphasize it to you. So, I think a
[14:28] (868.48s)
lot of people, you know, me included,
[14:30] (870.32s)
when we start at work, we're like
[14:31] (871.92s)
nervous and a little bit scared. And for
[14:34] (874.16s)
me, I thought if I ask too many
[14:35] (875.76s)
questions, they're going to think I'm
[14:36] (876.72s)
stupid, right? Uh, but I ended up kind
[14:39] (879.44s)
of like trying to be, you know,
[14:41] (881.68s)
delusional as possible and just being
[14:43] (883.04s)
like, "Okay, you know, I deserve to be
[14:45] (885.52s)
here. Like, I have to ask questions."
[14:47] (887.92s)
And I also kept trying to remind myself
[14:49] (889.76s)
that it's better to ask questions and
[14:52] (892.24s)
learn and for them to think you're
[14:53] (893.76s)
stupid than to not ask questions. Never
[14:56] (896.16s)
learn anything, you know, and stay dumb,
[14:58] (898.00s)
right? I also remember thinking like,
[14:59] (899.28s)
I'm just going to try to learn as much
[15:00] (900.24s)
as I can here and if I get fired, I get
[15:01] (901.84s)
fired, but at least I learned. So,
[15:03] (903.68s)
definitely try to ask a lot of
[15:04] (904.96s)
questions. I think you know I have
[15:07] (907.20s)
mentored and grown a lot of engineers
[15:09] (909.68s)
and what I think is the biggest
[15:12] (912.24s)
differentiator between those that like
[15:14] (914.08s)
really succeed really quickly and the
[15:15] (915.52s)
ones that don't is how quickly they're
[15:17] (917.20s)
able to start asking questions,
[15:18] (918.96s)
learning, being curious, unblocking
[15:21] (921.20s)
themselves and really taking that
[15:22] (922.56s)
initiative to figure things out. One
[15:24] (924.24s)
thing I can add to that is I've had five
[15:26] (926.64s)
interns at Meta and some of them were
[15:30] (930.24s)
rock stars, some of them were not. When
[15:32] (932.56s)
I think about the ones that were rock
[15:34] (934.16s)
stars, they had the the audacity to
[15:37] (937.68s)
propose improvements. Even though
[15:39] (939.68s)
obviously I'm the more senior person,
[15:42] (942.00s)
they had the audacity to ask questions,
[15:44] (944.88s)
propose improvements. Sometimes they
[15:46] (946.80s)
weren't right, but I could see the
[15:48] (948.48s)
logic, but many times they were. And
[15:50] (950.88s)
when I think of the low performers, I
[15:52] (952.48s)
remember oftent times they were like
[15:54] (954.32s)
quieter. They kind of I didn't know
[15:55] (955.84s)
exactly what their progress was. I could
[15:58] (958.16s)
tell they were trying really hard to
[16:00] (960.32s)
figure things out without my help and
[16:02] (962.64s)
then time would pass, time would pass
[16:03] (963.92s)
and they're making no progress and then
[16:05] (965.92s)
you know at the end they're not going to
[16:07] (967.04s)
get a return offer. So asking question
[16:09] (969.68s)
especially when you're junior is is
[16:11] (971.52s)
really important. What if I feel like
[16:13] (973.76s)
I'm asking too many questions and I feel
[16:15] (975.52s)
like I'm not a good and bad.
[16:19] (979.60s)
So my perception really successful
[16:21] (981.68s)
software engineers were in college was
[16:24] (984.16s)
that they were just like the super
[16:26] (986.16s)
genius um you know they're probably the
[16:28] (988.56s)
ones setting the curve in the class in
[16:30] (990.48s)
my CS classes but that's actually not
[16:32] (992.32s)
true. Like I think what uh makes a
[16:34] (994.72s)
really good software engineer is having
[16:36] (996.80s)
a decent level of technical skills of
[16:38] (998.96s)
course but also having a decent level of
[16:41] (1001.68s)
soft skills. you know, learning how to
[16:43] (1003.44s)
persuade others, how to influence
[16:45] (1005.36s)
others, because if you think about what
[16:47] (1007.76s)
you're doing at work, it's like this
[16:49] (1009.28s)
giant group project, right? And you
[16:51] (1011.04s)
know, I'm sure you all have had group
[16:52] (1012.48s)
projects and you probably also had
[16:54] (1014.40s)
tension at times in the group project,
[16:56] (1016.16s)
right? You want it, you want things to
[16:58] (1018.24s)
be done this way, but this other person
[16:59] (1019.76s)
is like, "No." And at work, if you just
[17:02] (1022.56s)
tell, you know, whoever you disagree
[17:04] (1024.24s)
with, like your idea is trash. You know,
[17:06] (1026.48s)
we should do it my way, they're not
[17:07] (1027.68s)
going to listen to you, right? So, a
[17:09] (1029.28s)
better way to kind of communicate that
[17:10] (1030.56s)
message is, hey, you know, I see that
[17:13] (1033.12s)
you're pretty frustrated. You know, I
[17:14] (1034.96s)
see your point of view and why you would
[17:17] (1037.68s)
want this solution. Perhaps we can
[17:19] (1039.52s)
compromise and find um, you know,
[17:22] (1042.80s)
negotiate like some in between or some
[17:24] (1044.72s)
middle ground. What do you think? They
[17:26] (1046.40s)
are much more willing to buy into your
[17:27] (1047.92s)
idea and you're ultimately able to kind
[17:29] (1049.76s)
of ship things and get things launched.
[17:31] (1051.84s)
Um, and that is, you know, I would say
[17:35] (1055.04s)
just as important as being a really
[17:37] (1057.12s)
great coder and understanding the
[17:38] (1058.80s)
technical parts. Yeah, definitely. I
[17:40] (1060.60s)
think one thing that surprised me, which
[17:44] (1064.16s)
I never thought would be true until I
[17:46] (1066.40s)
got to the industry, was that how little
[17:49] (1069.92s)
of the job was actually the code. So,
[17:52] (1072.88s)
uh, code is is really important, too. So
[17:55] (1075.28s)
they're both important, but I think when
[17:57] (1077.36s)
you get into the industry, you'll see
[17:59] (1079.60s)
there's all this other stuff around the
[18:01] (1081.68s)
code that matters a lot. Obviously, the
[18:03] (1083.68s)
people are important because it is kind
[18:05] (1085.60s)
of like a giant collaborative project.
[18:07] (1087.76s)
Like there's thousands of engineers
[18:09] (1089.60s)
working on this software. Um, but
[18:11] (1091.68s)
there's all these other things too, like
[18:13] (1093.12s)
you have to write a ton. Like I hated
[18:14] (1094.96s)
writing in high school. Felt like not
[18:17] (1097.76s)
objective, like not as I don't know. I
[18:20] (1100.40s)
just didn't like writing. Um but when I
[18:23] (1103.04s)
got into industry like I realized
[18:24] (1104.96s)
writing is the job. It's how you
[18:27] (1107.20s)
influence others. Every code change has
[18:29] (1109.68s)
all this writing around it. Like you got
[18:31] (1111.12s)
to write design doc before you start you
[18:33] (1113.36s)
know get feedback. Then when you
[18:35] (1115.28s)
actually writing the code you write a
[18:37] (1117.52s)
description of the code. Then when you
[18:39] (1119.04s)
land it and it has results you write
[18:41] (1121.04s)
results. You write about the results and
[18:43] (1123.52s)
you know share that with the group. So
[18:45] (1125.36s)
there's there's so much around the code
[18:48] (1128.08s)
that you need to do that's about people
[18:50] (1130.32s)
and communication and I think if you're
[18:53] (1133.28s)
not is is this a common term gigachad
[18:56] (1136.00s)
gigachad coder if you're not a gigachad
[18:59] (1139.16s)
coder you should be fine I think
[19:01] (1141.44s)
everyone in this room if you go to UCLA
[19:03] (1143.52s)
you're smart enough to be writing code
[19:05] (1145.92s)
that matters and then a lot of what's
[19:08] (1148.96s)
going to make you excel is actually not
[19:10] (1150.40s)
about the code and your technical skills
[19:12] (1152.72s)
related to that have you guys ever
[19:14] (1154.96s)
gotten imposttor syndrome? Imposter
[19:17] (1157.04s)
syndrome. My best friend been with me my
[19:19] (1159.04s)
whole career. So I think a lot of people
[19:22] (1162.64s)
get imposter syndrome. I think it's very
[19:24] (1164.24s)
normal. I think a majority of people
[19:26] (1166.16s)
actually do get imposter syndrome. And I
[19:28] (1168.56s)
think it's a little bit odd too because
[19:30] (1170.40s)
I remember when I first joined Google,
[19:32] (1172.32s)
right? I passed the interviews, I did my
[19:34] (1174.80s)
internship, they gave me a passing grade
[19:36] (1176.88s)
on my internship and then I got there
[19:38] (1178.88s)
and I was like, "Oh shit." you know,
[19:40] (1180.96s)
like everyone everyone look everyone's
[19:42] (1182.88s)
gig Chad Coder here. Um, how am I going
[19:45] (1185.36s)
to make it? Surprisingly, even after I
[19:47] (1187.28s)
got promoted, after I got promoted, I
[19:49] (1189.36s)
was still like, uh, maybe like they
[19:51] (1191.52s)
promoted me wrong. Like maybe I got
[19:53] (1193.20s)
promoted too early. And then after I got
[19:55] (1195.60s)
promoted after that, I was like, oh man,
[19:57] (1197.52s)
this well maybe not the last promotion,
[19:59] (1199.92s)
maybe they like screwed up this
[20:01] (1201.20s)
promotion. And I had it a lot um during
[20:04] (1204.32s)
my career. And you know, it's pretty
[20:06] (1206.72s)
normal, right? Um, the best way I kind
[20:09] (1209.36s)
of got around it is, you know, again
[20:12] (1212.24s)
going back to that mindset of all right,
[20:14] (1214.96s)
even if I'm like not meant to be here,
[20:16] (1216.80s)
like let's try to learn as much as I
[20:18] (1218.24s)
can, do the best job I can, and you
[20:20] (1220.40s)
know, if I get fired, then at least I
[20:21] (1221.92s)
learned a lot. But, you know, I never
[20:23] (1223.44s)
got fired luckily. Uh, but impositive
[20:26] (1226.16s)
syndrome definitely still comes here and
[20:28] (1228.16s)
then in waves too. Um, but you know, I
[20:31] (1231.68s)
think eventually uh you get over it and
[20:34] (1234.16s)
everyone it's really an experience that
[20:36] (1236.40s)
a lot of people have. So don't feel
[20:37] (1237.92s)
stressed out if you ever feel that way
[20:39] (1239.20s)
at work. Did you ever have any feedback
[20:41] (1241.28s)
that made you feel because you must have
[20:43] (1243.68s)
been Yeah. just getting good marks. Like
[20:45] (1245.92s)
how do you how's it possible that you
[20:47] (1247.84s)
felt not good even though you were
[20:49] (1249.92s)
getting all this praise? I was just an
[20:51] (1251.76s)
anxious little kid, you know? I was I
[20:54] (1254.72s)
was just always stressed out about work.
[20:56] (1256.80s)
And I remember one time in a one-on-one
[20:59] (1259.28s)
with my manager, you know, he like
[21:01] (1261.36s)
looked at me a little bit and he was
[21:02] (1262.56s)
like, "You need to chill, you know, like
[21:04] (1264.48s)
you're doing okay, you know, calm down."
[21:06] (1266.40s)
And I was like, are you sure though?
[21:07] (1267.76s)
Like, you know, seems sus.
[21:10] (1270.84s)
Um, I think part of it was also because,
[21:13] (1273.68s)
you know, when they say you're doing
[21:14] (1274.88s)
well, I'm like, I was, but is this like
[21:17] (1277.36s)
a C passing doing well or is this like a
[21:19] (1279.60s)
A+ doing well? You know, like and I
[21:22] (1282.56s)
think eventually I did kind of learn um,
[21:25] (1285.36s)
you know, through talking with my
[21:26] (1286.48s)
manager and understanding the
[21:28] (1288.64s)
expectations for myself of like kind of
[21:30] (1290.88s)
how I was doing and I was a little bit
[21:33] (1293.20s)
less stressed after that. But yeah, I
[21:35] (1295.44s)
was pretty stressed in the beginning.
[21:37] (1297.52s)
How do you advocate for yourself while
[21:40] (1300.56s)
at work? This will be helpful for your
[21:42] (1302.24s)
whole career, but if you're also going
[21:43] (1303.92s)
into internship, this could be helpful.
[21:45] (1305.76s)
In order to get promoted, there's kind
[21:48] (1308.40s)
of like two pieces to it. One is you do
[21:50] (1310.88s)
good work. That's obvious. Like we all
[21:52] (1312.80s)
know do good work, you're going to get
[21:54] (1314.08s)
promoted. But actually, promotions are
[21:57] (1317.92s)
ultimately decided by people. They need
[21:59] (1319.92s)
to know about your work. And if you go
[22:01] (1321.60s)
and build this amazing feature that
[22:04] (1324.08s)
nobody knows about, doesn't matter how
[22:06] (1326.80s)
good it is, no, you're not going to get
[22:08] (1328.16s)
any recognition for it. And so, you
[22:10] (1330.48s)
know, how do you advocate for yourself
[22:12] (1332.56s)
after you've done great work, which is
[22:14] (1334.56s)
the hard part? There's just this last
[22:16] (1336.64s)
step that you got to do this last few%
[22:18] (1338.40s)
of your time where you maybe you either
[22:21] (1341.52s)
write about it or you present it in a
[22:24] (1344.72s)
meeting or something like that where you
[22:25] (1345.92s)
say, "Hey, hey everyone." Let's say
[22:27] (1347.28s)
you're at an internship. You have your
[22:29] (1349.04s)
intern project. you hit a big milestone
[22:31] (1351.28s)
rather than just like quietly moving on
[22:32] (1352.96s)
to the next step. Maybe in your
[22:34] (1354.96s)
one-on-one you tell your intern manager
[22:36] (1356.96s)
like, "Hey, I did it and it's like a few
[22:39] (1359.44s)
weeks early. I'm ready for the next
[22:40] (1360.72s)
thing." Like that kind of puts the thing
[22:42] (1362.16s)
in the idea in their head. Also, maybe
[22:44] (1364.88s)
you make a post internally. You write an
[22:47] (1367.44s)
email or something that says like, "Hey,
[22:49] (1369.68s)
you know, update on my project. It's
[22:51] (1371.60s)
done. I'm I'm killing it. Look, all the
[22:54] (1374.56s)
good results here." and you can write
[22:56] (1376.72s)
about it in a way that uh matters to to
[22:59] (1379.60s)
the audience. If you do that, people are
[23:02] (1382.40s)
going to get a sense of, okay, this
[23:04] (1384.08s)
person's killing it. And it will be kind
[23:06] (1386.48s)
of surprising if you didn't get a uh a
[23:08] (1388.80s)
return offer if you were doing so well
[23:10] (1390.96s)
and people knew that you were doing
[23:12] (1392.40s)
well. So, this is like specifically that
[23:15] (1395.52s)
last step, and I think a lot of people
[23:16] (1396.88s)
miss this, especially if they're more
[23:19] (1399.12s)
introverted or they're more quiet, is
[23:21] (1401.20s)
taking that last step on after the good
[23:23] (1403.44s)
work. You got to tell everyone about it.
[23:26] (1406.08s)
And writing is one of the best ways to
[23:28] (1408.08s)
do it, but maybe, you know, it depends
[23:30] (1410.00s)
on your specific team setup. Maybe you
[23:32] (1412.40s)
can talk about the work in standup or,
[23:34] (1414.64s)
you know, whatever meeting. The term
[23:36] (1416.16s)
that we often use at work is visibility.
[23:38] (1418.96s)
So, you want to have visibility for
[23:41] (1421.52s)
yourself. It could be doing it by
[23:43] (1423.20s)
yourself as Ryan said, but it can also
[23:45] (1425.12s)
be doing it through other means like
[23:46] (1426.64s)
through your manager or through, you
[23:48] (1428.64s)
know, whoever else you're working with
[23:50] (1430.00s)
like a PM. Um, they can also help you
[23:52] (1432.40s)
get visibility, but it is really
[23:54] (1434.20s)
important. I forgot, but there's some
[23:56] (1436.96s)
analogy where it's like, oh, you know,
[23:58] (1438.24s)
if a tree falls in a forest and no one
[24:00] (1440.24s)
sees it, did it really fall? So, I also
[24:02] (1442.64s)
think I have to think about that, you
[24:04] (1444.08s)
know, like just because you did a
[24:05] (1445.28s)
project, people you also need to get
[24:06] (1446.88s)
people to know that you did that
[24:08] (1448.00s)
project, right? Um otherwise in the
[24:10] (1450.00s)
future like you're trying to get
[24:11] (1451.20s)
promoted and you're like oh I did this
[24:14] (1454.24s)
and you know whoever is helping you get
[24:15] (1455.84s)
promoted is like no one knows like how
[24:18] (1458.00s)
how is anyone supposed to know? Uh so
[24:19] (1459.84s)
it's definitely important to get
[24:21] (1461.12s)
visibility for yourself at work. So I
[24:22] (1462.80s)
think a lot of people are interested in
[24:24] (1464.72s)
this postcribe which is better tech or
[24:28] (1468.88s)
startups.
[24:30] (1470.72s)
Yeah. Okay. Actually I'm curious what
[24:32] (1472.56s)
what do you guys think? Okay. So between
[24:34] (1474.72s)
big tech versus startups, if you had
[24:37] (1477.20s)
both options, if you would pick big
[24:40] (1480.08s)
tech, raise your
[24:42] (1482.52s)
hand. Okay, that's like the whole
[24:45] (1485.16s)
room. Okay. And then anyone startups?
[24:48] (1488.40s)
How about for startups? All right, we
[24:50] (1490.40s)
got a few a few people. For anyone who's
[24:53] (1493.60s)
going to or would want to go to a
[24:55] (1495.20s)
startup, are you willing to answer why?
[24:57] (1497.20s)
I'm kind of curious. I'm a first movers
[24:59] (1499.84s)
revive. So like if the cup pig gets
[25:01] (1501.84s)
really big, you'll have a lot of
[25:07] (1507.08s)
that you fail, but it always go work for
[25:10] (1510.00s)
big tech. Yeah, I see another hand
[25:12] (1512.24s)
raised. Yeah, whoever changes you might
[25:14] (1514.88s)
like more fun facing and like might make
[25:17] (1517.52s)
more of an impact like proportional to
[25:19] (1519.36s)
the company. Yeah, because I think the
[25:21] (1521.52s)
impact is probably going to be smaller
[25:23] (1523.52s)
because of the scale of big tech, but
[25:25] (1525.68s)
proportional to company it'll be bigger,
[25:27] (1527.28s)
I'm sure. Yeah. Um Okay. Yeah, that's
[25:30] (1530.16s)
that's really interesting. So, okay, big
[25:32] (1532.56s)
tech versus startups. Um, before saying
[25:35] (1535.12s)
like the answer that I would give, I
[25:38] (1538.00s)
think there's some differences, right?
[25:39] (1539.36s)
Like, you know, big tech versus
[25:41] (1541.04s)
startups, one of the biggest things is
[25:44] (1544.16s)
the prestige or like the some people
[25:46] (1546.48s)
call it like brand equity. If you go to
[25:49] (1549.84s)
a name that is known and then anyone
[25:52] (1552.64s)
sees that resume, it's you're at least
[25:55] (1555.92s)
that good. You're at least that bar. So
[25:58] (1558.56s)
it makes it a lot easier to get hired.
[26:01] (1561.44s)
So if you go to a startup that people
[26:04] (1564.24s)
don't know, then you wouldn't have that
[26:06] (1566.56s)
prestige. There's startups, there's a
[26:08] (1568.80s)
whole range of startups, though. There
[26:10] (1570.24s)
are very well-known startups, too. Like
[26:12] (1572.40s)
imagine a company like Perplexity or
[26:14] (1574.64s)
something. Um, you know, that's a hot
[26:16] (1576.56s)
startup. Maybe you would still get some
[26:18] (1578.32s)
of that prestige. But I definitely
[26:20] (1580.16s)
remember I went to Amazon, which was, I
[26:23] (1583.28s)
guess, the lowest tier of the Fang ones
[26:26] (1586.00s)
at the time. Probably still true. I
[26:27] (1587.44s)
don't know. But um a after I I went
[26:31] (1591.12s)
there like my LinkedIn was blowing up
[26:33] (1593.12s)
and I was not writing on LinkedIn or
[26:34] (1594.56s)
anything. Just so many recruiters just
[26:36] (1596.40s)
coming in random emails. So I do think
[26:40] (1600.32s)
that's worth something for sure. The the
[26:43] (1603.04s)
first mover advantage that's
[26:44] (1604.40s)
interesting. One thing that I'll say is
[26:46] (1606.16s)
generally true for big tech versus
[26:47] (1607.84s)
startups across career growth across
[26:50] (1610.56s)
your compensation is it's a trade-off
[26:53] (1613.68s)
between high expected value versus high
[26:56] (1616.96s)
variance. So what I mean by that is um
[27:01] (1621.76s)
if you go to big tech you're in the
[27:04] (1624.40s)
average case you're probably going to be
[27:06] (1626.64s)
doing better but you will never have a
[27:10] (1630.00s)
moon up or a moon down scenario. So if
[27:13] (1633.84s)
you wanted to like you know the jobs we
[27:16] (1636.64s)
work today will never be like rich rich
[27:18] (1638.48s)
rich like you I mean you'll make a good
[27:20] (1640.56s)
amount. You make a good amount. It it's
[27:22] (1642.56s)
it's it's a good amount. I'm very happy.
[27:24] (1644.56s)
No no it's great but like you want like
[27:27] (1647.44s)
$10 million or something. You're not
[27:29] (1649.52s)
going to get like that level of rich by
[27:31] (1651.68s)
you know working in big tech. Whereas at
[27:33] (1653.52s)
a startup you can you know boom or bust.
[27:35] (1655.60s)
You could your company could be dead
[27:37] (1657.12s)
tomorrow. you could also be, you know,
[27:39] (1659.92s)
making, you know, eight figures or nine
[27:42] (1662.32s)
figures in like some crazy cases if
[27:44] (1664.08s)
you're a founder. So, and then that's
[27:46] (1666.40s)
also true for your career. If you were
[27:48] (1668.24s)
there early and it moons, you you'll be,
[27:52] (1672.32s)
you know, like a director really fast.
[27:54] (1674.16s)
You'll you'll go and like VP like some
[27:56] (1676.16s)
crazy thing where, you know, you could
[27:58] (1678.56s)
never do that in big tech. So, it it
[28:01] (1681.04s)
kind of depends. Yeah. I personally
[28:03] (1683.60s)
chose big tech. Um, like at the time I
[28:06] (1686.24s)
had a couple of startup offers and I
[28:07] (1687.92s)
also had a couple big tech
[28:09] (1689.96s)
offers and honest part of the reason is
[28:12] (1692.56s)
I'm just lazy. You know, I'm a good work
[28:14] (1694.48s)
life balance. Like I sold my soul to
[28:17] (1697.04s)
Google so that I could live a good life.
[28:19] (1699.12s)
Um, so I'm so glad I chose big tech cuz
[28:22] (1702.48s)
you know some of my friends they are at
[28:23] (1703.84s)
startups. They're working so hard and in
[28:26] (1706.16s)
some cases I think some of them did moon
[28:28] (1708.24s)
you know go crazy and some of them did
[28:30] (1710.80s)
not so crazy. And you know, I'm very
[28:34] (1714.64s)
happy I chose big tech. Uh it's actually
[28:37] (1717.52s)
really interesting because uh at UCLA,
[28:40] (1720.72s)
it seems like there is there are a lot
[28:44] (1724.24s)
of there's a big portion of people that
[28:45] (1725.76s)
want to go into big tech much more than
[28:48] (1728.32s)
other colleges. Um and I've always
[28:51] (1731.44s)
wondered why because I've also talked to
[28:53] (1733.60s)
some recruiters from startups and they
[28:55] (1735.60s)
say, "Yeah, we always go to UCLA to
[28:57] (1737.36s)
recruit uh but no one seems to want to
[28:59] (1739.36s)
join so we kind of stop going." Which
[29:01] (1741.28s)
colleges are the startup colleges?
[29:02] (1742.88s)
You're talking, you know, like um
[29:04] (1744.28s)
Stanford, I think. Um they said a lot of
[29:07] (1747.04s)
Ivy ones, so I'm like, is it just
[29:09] (1749.12s)
because you know, like they're rich, so
[29:11] (1751.60s)
they don't need to make money
[29:13] (1753.04s)
immediately after college? I don't know.
[29:14] (1754.56s)
You know, these are just my
[29:16] (1756.28s)
guesses. Yeah. Uh one last thing I would
[29:18] (1758.88s)
say is the learnings can be different.
[29:23] (1763.08s)
So in in big tech, you're learning from
[29:26] (1766.88s)
the industry best practices that are
[29:28] (1768.72s)
already set. So it's kind of like you're
[29:31] (1771.92s)
not going to have a lot of variance.
[29:32] (1772.88s)
You're going to get you're going to
[29:33] (1773.92s)
learn the good stuff. Uh it's it's all
[29:36] (1776.32s)
there like you know all the the best
[29:38] (1778.08s)
practices in startups. It can vary
[29:40] (1780.88s)
widely like your role could could be
[29:43] (1783.12s)
even less code. It could be maybe more
[29:45] (1785.84s)
PM stuff. It can be more a bunch of
[29:47] (1787.92s)
things. Whatever the startup needs at
[29:49] (1789.44s)
the moment depending on how small it is.
[29:51] (1791.92s)
Yeah. I think it's I guess it's similar
[29:53] (1793.68s)
to the variance thing. you could learn a
[29:55] (1795.12s)
lot too from a crazy startup trajectory,
[29:58] (1798.40s)
but also there can be a case where it
[30:00] (1800.32s)
crashes and burns and you don't learn as
[30:02] (1802.48s)
much. So, um, so yeah, I mean, you know,
[30:05] (1805.52s)
in conclusion, I've I picked big tech
[30:08] (1808.72s)
and I I would recommend big tech at
[30:11] (1811.12s)
least for the first few years of your
[30:14] (1814.16s)
career. You get just huge jump in
[30:16] (1816.56s)
prestige. You just lock that in and then
[30:18] (1818.80s)
you can do whatever you want after that.
[30:20] (1820.08s)
just learn all the basics in big tech,
[30:22] (1822.16s)
get that early signing bonus and then
[30:25] (1825.04s)
you know rest of your life do you can go
[30:26] (1826.80s)
you know do do whatever. And as interns
[30:30] (1830.96s)
and as I guess future new grads how do
[30:34] (1834.96s)
people managers like you guys measure by
[30:37] (1837.68s)
impact? Impact is just another way of
[30:40] (1840.88s)
saying the concrete measurable outcomes
[30:44] (1844.32s)
of your work. And the tech industry is
[30:47] (1847.04s)
great because you're not compensated or
[30:49] (1849.92s)
rewarded based off of the time you spend
[30:52] (1852.56s)
or like your years of experience. Like
[30:54] (1854.72s)
literally, you know, how much money did
[30:57] (1857.04s)
you make or how much you how much faster
[30:59] (1859.36s)
did you make that important customer
[31:01] (1861.68s)
flow or whatever whatever it is. And so
[31:04] (1864.08s)
when we talk about impact, it's it's
[31:06] (1866.80s)
specifically that. And so my answer to
[31:09] (1869.36s)
this question would be it really depends
[31:11] (1871.20s)
on the team you're on. If you're working
[31:13] (1873.68s)
on ads, it's going to be money. If
[31:16] (1876.80s)
you're working on some infrastructure
[31:19] (1879.12s)
team and maybe it's the cost of the
[31:20] (1880.96s)
database or maybe it's the latency of
[31:23] (1883.44s)
the database. If you're working on like
[31:25] (1885.44s)
a growth team, it might be like daily
[31:27] (1887.28s)
active users or percentage of people
[31:30] (1890.08s)
that sign up on some critical customer
[31:32] (1892.16s)
flows. So, it really depends. I think
[31:34] (1894.24s)
the main thing is like wherever you go,
[31:36] (1896.80s)
you should 100% learn this from your
[31:39] (1899.52s)
team and your manager because this is
[31:41] (1901.44s)
how you're rewarded. Yeah. Like if you
[31:43] (1903.20s)
write like 10k lines of code and it's
[31:46] (1906.16s)
all fixing typos in the codebase,
[31:48] (1908.08s)
there's no impact, right? Like you know,
[31:50] (1910.24s)
I mean there's a little bit of impact,
[31:51] (1911.76s)
the tiniest bit, but it's nothing
[31:53] (1913.52s)
compared to, you know, for example, if
[31:55] (1915.52s)
you were somehow able to 2x the revenue
[31:57] (1917.76s)
of the company, right? That's that's
[31:59] (1919.60s)
probably something that people care a
[32:00] (1920.88s)
little bit more about. Yeah, like one
[32:03] (1923.36s)
line code change that makes a 20%
[32:06] (1926.08s)
improvement and something that matters
[32:08] (1928.16s)
versus like hundreds of thousands of
[32:10] (1930.40s)
lines doing random things no one cares
[32:12] (1932.84s)
about. Always want the oneline change
[32:15] (1935.44s)
and that's true pretty much at every
[32:17] (1937.04s)
company. Do you need an MBA for
[32:20] (1940.56s)
engineering management? Yeah. So, this
[32:22] (1942.08s)
is an interesting question. I remember
[32:23] (1943.44s)
when I was in college, I had the same
[32:25] (1945.04s)
question cuz I didn't know. And I think
[32:27] (1947.76s)
you just learn from maybe the older
[32:29] (1949.52s)
generation that MBAs are good for some
[32:32] (1952.20s)
reason. I I don't have an MBA. I'm a
[32:35] (1955.20s)
manager. You don't have an NBA. You're a
[32:37] (1957.44s)
manager. But actually, I was working on
[32:39] (1959.92s)
some research with some company to like
[32:42] (1962.08s)
answer this question. I actually have
[32:43] (1963.84s)
this graph. It's kind of interesting.
[32:45] (1965.68s)
So, this company basically scrapes
[32:48] (1968.80s)
LinkedIn to pull data. And you can see
[32:51] (1971.44s)
here they pulled like a few thousand
[32:54] (1974.16s)
data points from LinkedIn about
[32:55] (1975.92s)
directors and higher. And you can see
[32:58] (1978.32s)
with and without MBA it's comp
[33:00] (1980.52s)
comparable. Um and actually without MBA
[33:03] (1983.28s)
is even lower. So yeah, you don't need
[33:05] (1985.60s)
NBA. Yeah, it's really odd. Like my mom,
[33:07] (1987.76s)
she she kept telling me to get an NBA.
[33:09] (1989.52s)
Like I had to get an MBA and I didn't
[33:11] (1991.44s)
understand why. But I think also it's
[33:13] (1993.44s)
because like my mom's, you know, an
[33:15] (1995.84s)
immigrant and she's like, "Ah, UCLA is
[33:18] (1998.32s)
not good enough. me Ivy, you know, like
[33:20] (2000.40s)
Stanford, you got to go Stanford. You
[33:21] (2001.92s)
got to get an MBA at Stanford. Um, and
[33:24] (2004.56s)
she she always like I don't know why she
[33:27] (2007.36s)
was my hater. She would have she
[33:28] (2008.72s)
believed I wouldn't succeed without an
[33:30] (2010.36s)
MBA. Uh, now she's a little bit like uh
[33:33] (2013.76s)
more chilled because uh I got the staff,
[33:36] (2016.40s)
right? And then she's like looking on
[33:38] (2018.48s)
Xiaoongu like little red book and she's
[33:40] (2020.64s)
like, "Oh wait, that's kind of
[33:42] (2022.80s)
good."
[33:45] (2025.68s)
And I guess we're all suffering through
[33:47] (2027.28s)
this right now. So, we're wondering for
[33:48] (2028.96s)
you guys, how was college recruiting?
[33:52] (2032.72s)
Definitely wanted to have a black hole
[33:55] (2035.04s)
swallow me at times. Um, college
[33:57] (2037.36s)
recruiting was definitely, you know,
[33:59] (2039.20s)
hard. I'm sure all of y'all have um kind
[34:01] (2041.76s)
of endured it. Um, I think one of my
[34:05] (2045.68s)
recommendations or like a story I like
[34:07] (2047.68s)
to share with y'all about my college
[34:09] (2049.20s)
recruiting experience is with Yelp. So,
[34:12] (2052.48s)
I had a I had a technical coding
[34:15] (2055.60s)
challenge with Yelp, right? They sent me
[34:17] (2057.20s)
I don't know. a hacker ring thing and I
[34:19] (2059.60s)
opened it. I was doing C++ because you
[34:21] (2061.92s)
know how we all learn C++ to start and
[34:23] (2063.52s)
that's all I knew at the time. God
[34:26] (2066.52s)
forbid and it was it was a merge step of
[34:30] (2070.00s)
a merge sort, right? And I just had to
[34:31] (2071.68s)
complete it and um like I did it I put
[34:35] (2075.04s)
it together. I'm pretty sure the code
[34:36] (2076.56s)
was right, but I was getting a seg fault
[34:38] (2078.08s)
and I did not did not know why. I kept
[34:40] (2080.40s)
track of my work and I really didn't get
[34:42] (2082.00s)
understand why I was not able to compile
[34:44] (2084.56s)
the code and it was a 15-minute coding
[34:46] (2086.32s)
challenge and then I failed and you know
[34:48] (2088.24s)
the Yelp recruiter came out um and I was
[34:50] (2090.64s)
like okay Ricky so goodbye um and I
[34:54] (2094.16s)
emailed her back and I say come on like
[34:56] (2096.16s)
I knew this I knew this hacker rig it
[34:58] (2098.64s)
was the mer of a merge sort tell your
[35:00] (2100.48s)
engineer to look at it because I'm 100%
[35:02] (2102.64s)
it's sure it's right and she did and she
[35:05] (2105.84s)
said the engineer took a look at it it
[35:07] (2107.52s)
looks correct actually Um, here's
[35:10] (2110.08s)
another coding challenge to do again.
[35:12] (2112.08s)
And that one I passed and I was able to
[35:15] (2115.28s)
move on to the interview round. So, uh,
[35:17] (2117.68s)
that was an example of me persevering
[35:19] (2119.28s)
through college recruiting. So, you
[35:21] (2121.52s)
know, it's worth it, uh, to kind of push
[35:23] (2123.76s)
back sometimes and advocate your for
[35:25] (2125.52s)
yourself during the college recruiting
[35:26] (2126.96s)
process. Yeah, for your first role,
[35:29] (2129.68s)
that's the hardest one. No doubt. You
[35:32] (2132.56s)
have to be scrappy. like what you did
[35:34] (2134.80s)
there like you you literally got
[35:36] (2136.40s)
rejected and you said
[35:38] (2138.84s)
no like look at it again and so you yeah
[35:42] (2142.48s)
you kind of have to do stuff once you
[35:43] (2143.92s)
get the first one it's a lot easier um
[35:46] (2146.72s)
but yeah definitely want to be scrappy
[35:49] (2149.04s)
and I think like yeah my first role was
[35:50] (2150.88s)
like some random company um I don't even
[35:54] (2154.00s)
put on my resume anymore but uh it
[35:56] (2156.48s)
helped me get some experience so then
[35:58] (2158.64s)
the next time I my resume looked a bit
[36:01] (2161.36s)
better and then I could shoot some shots
[36:02] (2162.80s)
of some better companies. Yeah, I think
[36:04] (2164.88s)
it's tough the first one, but you know,
[36:06] (2166.64s)
if you can get a little scrappy or like
[36:08] (2168.48s)
take on some companies that are not as
[36:10] (2170.24s)
desirable, that can help a lot. People's
[36:12] (2172.24s)
number one
[36:13] (2173.56s)
motivation, as much money as possible.
[36:16] (2176.56s)
Yeah, this is interesting cuz I feel
[36:18] (2178.08s)
like I didn't I had no idea about money
[36:21] (2181.20s)
when I was in college. Like actually my
[36:23] (2183.36s)
life goal at some point was like, oh, I
[36:25] (2185.92s)
want to make 200K one day eventually.
[36:29] (2189.68s)
And then I have some people. Yeah, I was
[36:32] (2192.08s)
actually I was eating uh I was eating
[36:34] (2194.32s)
lunch with uh Daniel and you were saying
[36:37] (2197.92s)
you looked behind you and you're like,
[36:39] (2199.36s)
"Oh, 200k is like minimum wage in the
[36:43] (2203.16s)
Area." Um so yeah, but it's interesting
[36:46] (2206.96s)
that you guys asked this. Um how do you
[36:49] (2209.20s)
make as much money as possible? I would
[36:51] (2211.56s)
say that it depends on how much money
[36:56] (2216.40s)
you want to make. If you want to be rich
[36:58] (2218.84s)
rich, then you don't do what Ricky and I
[37:02] (2222.80s)
are doing. Um, yeah. If you want to be
[37:05] (2225.36s)
truly rich, you probably go to big tech
[37:07] (2227.12s)
for a few years and then you start your
[37:09] (2229.36s)
own company because that that's like the
[37:10] (2230.96s)
only way to make, you know, super crazy
[37:13] (2233.36s)
amounts. I mean, would you say something
[37:15] (2235.20s)
different or it's like if you want to be
[37:17] (2237.60s)
like 50 mil rich, right? You probably
[37:20] (2240.48s)
have to go crazy with something. you got
[37:22] (2242.80s)
to, you know, join OpenAI as employee
[37:25] (2245.84s)
number 50 and then you can probably make
[37:28] (2248.16s)
it. Uh me and Ryan, maybe if we work
[37:31] (2251.84s)
hard for another 20, 30, 40 years, uh
[37:36] (2256.56s)
and we get promoted three more times,
[37:38] (2258.80s)
yeah, maybe we could get there and, you
[37:41] (2261.20s)
know, the stock market, whatever
[37:43] (2263.84s)
continues to do well. Um, but if you're,
[37:47] (2267.60s)
you know, if you're just having, if
[37:48] (2268.96s)
you're just looking to have like a good
[37:50] (2270.32s)
life and, you know, just buy, uh, the
[37:52] (2272.56s)
things that you want and, you know, go
[37:54] (2274.88s)
on vacation whenever you want, I would
[37:56] (2276.80s)
say definitely go into big tech and just
[37:58] (2278.88s)
do a good job there. Um, as me and Ryan
[38:01] (2281.20s)
did. So, we have some last words for
[38:03] (2283.92s)
you. Um, and what I would like to impart
[38:07] (2287.68s)
with you guys is that work is not
[38:10] (2290.56s)
everything. You know, like even though
[38:11] (2291.84s)
we're here talking about our careers and
[38:14] (2294.40s)
we look back at work careers and we're
[38:16] (2296.56s)
very proud of what we did, when I look
[38:18] (2298.56s)
back on my 20s, you know, now I'm in my
[38:20] (2300.72s)
late 20s, I don't The happiest moments
[38:23] (2303.28s)
for me were not when I was like working
[38:25] (2305.36s)
all the time or when I was getting
[38:27] (2307.12s)
promoted, right? The happiest times were
[38:29] (2309.52s)
like when I'm at Coachella and I'm
[38:31] (2311.92s)
really drunk and my shirt's off and I'm
[38:33] (2313.60s)
having fun with my friends. Um, those
[38:36] (2316.08s)
are the most memorable moments for me um
[38:39] (2319.20s)
in my 20s, right? So, even though you
[38:42] (2322.32s)
should work hard, right, and hopefully
[38:44] (2324.80s)
you can do a good job at your job,
[38:46] (2326.96s)
definitely make sure to also go have
[38:48] (2328.40s)
fun. You're in tech. Um, you'll graduate
[38:51] (2331.68s)
and then you'll have free will. I don't
[38:53] (2333.52s)
know, go see a Beyonce or Taylor Swift
[38:55] (2335.80s)
concert. Um, go travel if that's what
[38:58] (2338.88s)
you want to do. But, uh, make sure
[39:00] (2340.88s)
you're definitely a multiaceted person.
[39:03] (2343.28s)
You're not just, you know, like if I
[39:05] (2345.52s)
made Google my life and I got laid off,
[39:08] (2348.16s)
you know, I'd probably be crashing out,
[39:09] (2349.60s)
right? But if Google lays me off one
[39:11] (2351.28s)
day, I'll be fine because I know I'm so
[39:13] (2353.44s)
much more um than just, you know, some
[39:16] (2356.16s)
computer science guy. So that's advice
[39:18] (2358.88s)
that I want to impart with y'all. Yeah.
[39:20] (2360.56s)
I mean on on that point too I guess one
[39:22] (2362.64s)
another way to look at it is in every
[39:25] (2365.16s)
aspect every area there's like this
[39:28] (2368.88s)
general curve of like diminishing
[39:31] (2371.00s)
returns
[39:32] (2372.76s)
where if you put in a lot of extra stuff
[39:36] (2376.16s)
into some area that little by little the
[39:39] (2379.28s)
gains are going to go away and so
[39:41] (2381.52s)
actually if you're truly a maximizer you
[39:44] (2384.16s)
go everywhere where you can get more
[39:47] (2387.12s)
returns before it starts to diminish so
[39:49] (2389.36s)
I guess that could be another way to to
[39:51] (2391.44s)
look at it as well. Um, but yeah, thanks
[39:54] (2394.48s)
for for having us here. That's like the
[39:56] (2396.40s)
prepared questions. If you guys have any
[39:58] (2398.80s)
questions, too. We like let's get into
[40:00] (2400.72s)
those two. We don't need to to end it
[40:02] (2402.72s)
and we're happy to continue to answer,
[40:04] (2404.48s)
but those are the end of the slides.
[40:06] (2406.32s)
Yeah. So, we're going to open it up to
[40:08] (2408.32s)
audience questions, I guess. Like, what
[40:10] (2410.64s)
are your current goals like career or
[40:12] (2412.88s)
likewise?
[40:15] (2415.12s)
You seem like pretty successful. So,
[40:16] (2416.80s)
like my story is funny. So, I feel like
[40:18] (2418.96s)
Ryan scammed me because I actually feel
[40:22] (2422.64s)
like in the beginning I was not that
[40:24] (2424.56s)
career driven. Um, but then, you know,
[40:27] (2427.52s)
Ryan and I were roommates. Uh, and we're
[40:29] (2429.52s)
still roommates. And I feel like he
[40:31] (2431.20s)
would kind of like whisper in my ear and
[40:32] (2432.56s)
be like, "Oh, but what if we got
[40:33] (2433.92s)
promoted, bro?" Like, "Wouldn't that be
[40:35] (2435.92s)
dope?" And then I was like, "Oh, maybe I
[40:39] (2439.20s)
guess." Um, and you know, but I'm really
[40:42] (2442.00s)
thankful to Ryan, right? because uh I
[40:44] (2444.48s)
think back in the t uh back in the day
[40:46] (2446.72s)
when I saw these levels uh we also had
[40:49] (2449.84s)
data about you know how long it took to
[40:52] (2452.88s)
progress from each level right and I was
[40:55] (2455.44s)
like oh you know I'll just take my time
[40:57] (2457.12s)
kind of doing it but then Ryan I was
[40:59] (2459.12s)
like ah but what if we went faster than
[41:01] (2461.04s)
that um and I honestly didn't even think
[41:03] (2463.60s)
that it was possible right so uh but
[41:06] (2466.40s)
then I kind of asked my manager and my
[41:08] (2468.24s)
manager was also supportive of me and
[41:10] (2470.48s)
also kind of helped me and as she got
[41:12] (2472.72s)
promoted download it a lot quicker than
[41:14] (2474.72s)
average. I obviously tried, right? I
[41:16] (2476.80s)
obviously put in the work.
[41:19] (2479.64s)
Um, I I don't know how many of you guys
[41:21] (2481.92s)
are from the Bay Area. He He's from the
[41:24] (2484.16s)
barrier. He went to one of those intense
[41:26] (2486.00s)
high schools. Well, he he says that I
[41:28] (2488.88s)
tricked him. No, he has something inside
[41:31] (2491.36s)
of him. He's pretty competitive. I'll
[41:33] (2493.04s)
say grinding is kind of fun, but
[41:36] (2496.60s)
uh do you guys know where Monavista is?
[41:39] (2499.60s)
Oh my god. It's a Monav Vista kid right
[41:41] (2501.60s)
here. But I've been trying to get the
[41:43] (2503.52s)
motivista out of me. It was kind of
[41:45] (2505.04s)
like, oh, why not, right? Like, if I'm
[41:47] (2507.04s)
going to, if I can, like, why not try to
[41:49] (2509.44s)
do it? So, I did put in the effort and I
[41:52] (2512.00s)
did kind of succeed, but I wasn't I
[41:54] (2514.64s)
definitely wasn't like, I don't know,
[41:57] (2517.04s)
giving up all the other things in life
[41:58] (2518.56s)
that I wanted to do just to kind of uh
[42:01] (2521.04s)
continue on with my career. So, it seems
[42:02] (2522.96s)
like you guys have stayed at your same
[42:05] (2525.20s)
job for the most of your career uh at
[42:07] (2527.52s)
least for now. And I'm wondering like
[42:09] (2529.36s)
what you guys perspectives are on
[42:11] (2531.20s)
switching jobs every two three years
[42:12] (2532.88s)
with like for the money or for like more
[42:15] (2535.68s)
responsibilities promotion. Yeah. So I
[42:18] (2538.24s)
think I'm a really special case where
[42:20] (2540.16s)
since I have joined Google I've been on
[42:22] (2542.80s)
the same team and basically under the
[42:25] (2545.92s)
same manager and I really have not
[42:28] (2548.08s)
changed any teams. But that's not to say
[42:30] (2550.40s)
that I haven't like looked for other
[42:33] (2553.36s)
teams, right? I think what usually
[42:35] (2555.68s)
happened is I would look around and I
[42:38] (2558.24s)
would see that my grass is indeed
[42:40] (2560.32s)
greener. So for me at least, I stayed on
[42:43] (2563.36s)
the same team, but there's definitely
[42:45] (2565.04s)
this aspect of survivorship bias, right?
[42:47] (2567.28s)
Maybe other people on my team, they left
[42:49] (2569.36s)
because they weren't having as good of a
[42:51] (2571.20s)
time. So I think it really depends um
[42:54] (2574.40s)
case by case. I definitely think it's
[42:56] (2576.64s)
worth thinking about, you know, every
[42:58] (2578.40s)
six months, do I still want to be here?
[43:00] (2580.40s)
Are there still the right opportunities?
[43:02] (2582.00s)
Does my team support me? does a manager
[43:03] (2583.92s)
to support me? And you know, depending
[43:06] (2586.24s)
on your answer to those questions, you
[43:07] (2587.76s)
can think about leaving or staying.
[43:09] (2589.36s)
Yeah, I I've done a lot of research on
[43:11] (2591.84s)
this topic actually and it's it's kind
[43:13] (2593.92s)
of interesting. It's a little bit
[43:15] (2595.12s)
nuanced. So, uh actually maybe the level
[43:18] (2598.24s)
thing. Oh, okay. So, actually job
[43:21] (2601.72s)
hopping the common take you hear is like
[43:24] (2604.72s)
job hop for promos, you know,
[43:26] (2606.88s)
no-brainer. Do it. Actually, it really
[43:29] (2609.68s)
depends. So like if you want optimal
[43:32] (2612.40s)
career strategy, it would probably be
[43:35] (2615.60s)
that you aggressively try to job hop in
[43:38] (2618.24s)
this area while you're trying to get
[43:40] (2620.00s)
promoted and you just take the faster
[43:42] (2622.40s)
path because here like momentum does not
[43:45] (2625.36s)
matter that much. You're just trying to
[43:46] (2626.96s)
get to the next level. And so you know
[43:49] (2629.44s)
if I were to put it simply, you probably
[43:51] (2631.44s)
want to job hop in this area. But once
[43:54] (2634.64s)
you get down here, job hopping is
[43:56] (2636.48s)
actually counterproductive because first
[43:58] (2638.64s)
off, you're not no one's going to hire
[44:00] (2640.80s)
someone at E7 or L7. If you're an L6, it
[44:05] (2645.04s)
just does not happen. If anything,
[44:07] (2647.12s)
they're going to downlevel and you hire
[44:08] (2648.48s)
you here. So, it's not going to be an
[44:10] (2650.96s)
effective strategy. But also, a lot of
[44:13] (2653.52s)
the promotions at this level come from
[44:16] (2656.56s)
building credibility and trust and
[44:18] (2658.48s)
having a track record. And so, if you're
[44:20] (2660.32s)
just job hopping all the time, you're
[44:21] (2661.60s)
not going to have that momentum. and
[44:23] (2663.76s)
people are not going to know you. So, I
[44:24] (2664.96s)
would say, you know, optimal is like
[44:27] (2667.76s)
aggressively job hop, especially if
[44:29] (2669.36s)
things aren't good. Just go to some
[44:30] (2670.80s)
place that'll give you a promo if you
[44:32] (2672.56s)
can get one. But then here, you got to
[44:34] (2674.96s)
be a little more thoughtful. One last
[44:36] (2676.72s)
thing I'll add, which is kind of
[44:38] (2678.40s)
interesting, is I've seen I saw someone
[44:40] (2680.88s)
job hop from E5 to E7. I also saw
[44:44] (2684.96s)
someone drop hop from E5 to E6. And I
[44:47] (2687.92s)
think the way that they did that was
[44:50] (2690.32s)
really interesting. If you're in a
[44:52] (2692.40s)
well-measured ladder like I am, you you
[44:55] (2695.04s)
all know I'm right here and the
[44:56] (2696.56s)
recruiters know that too. No one's going
[44:58] (2698.64s)
to put me here. Uh at best they'll put
[45:00] (2700.88s)
me here. But if you left here, like
[45:03] (2703.84s)
let's say I left and I went to started
[45:06] (2706.40s)
my own company. I'm I'm like in this
[45:09] (2709.44s)
probability cloud somewhere, you know,
[45:11] (2711.28s)
like no one knows what I am. And so then
[45:13] (2713.68s)
you can come back in somewhere if the
[45:15] (2715.92s)
work you're doing matches the
[45:17] (2717.60s)
expectations of these levels, which is
[45:19] (2719.76s)
really rare. It's like an unusual thing
[45:21] (2721.68s)
you'll never hear anyone say, but I've
[45:23] (2723.68s)
seen some very rare data points of that.
[45:26] (2726.40s)
Um, so if you ever leave the ladder now,
[45:29] (2729.36s)
you you kind of created some some leeway
[45:32] (2732.16s)
here. Um, but it's it's very rare though
[45:34] (2734.72s)
to do something like I think that guy
[45:36] (2736.08s)
that went from 5 to 7. He left Google at
[45:38] (2738.88s)
5. He joined a startup. He wrote a book
[45:42] (2742.32s)
as well on JavaScript and then he came
[45:44] (2744.96s)
in at at at Meta as a as a E7 and then
[45:47] (2747.92s)
he continued to get promoted. Ricky, uh,
[45:50] (2750.08s)
what team are you in at Google? Uh, I'm
[45:53] (2753.12s)
in ads. I I describe my job as making
[45:55] (2755.84s)
ads pretty. Um, y'all have definitely
[45:58] (2758.48s)
seen my ads, even if you've had ad
[46:00] (2760.48s)
blocker, I'm sure. Um, maybe maybe
[46:03] (2763.92s)
afterwards I'll show you my beautiful
[46:05] (2765.28s)
ads. I've seen videos of very like
[46:08] (2768.16s)
prolific people in the industry coffing
[46:10] (2770.00s)
about how AI is going to be like a low
[46:12] (2772.88s)
to midlevel software engineer. Yeah, I
[46:15] (2775.20s)
was wondering how like the
[46:16] (2776.72s)
implementation of AI and is going to
[46:18] (2778.56s)
affect hiring for like entry level.
[46:22] (2782.72s)
As far as we see it today, um I think
[46:27] (2787.20s)
you know it's hard to say in the future,
[46:28] (2788.96s)
maybe in 10 10 years, 5 10 years. In the
[46:32] (2792.08s)
short term though, for all of you, even
[46:33] (2793.92s)
if you're a first year, I still think
[46:36] (2796.08s)
that it's not going to completely change
[46:38] (2798.08s)
things. There are still some things that
[46:39] (2799.84s)
it's difficult for LMS to do. And if
[46:42] (2802.88s)
anything, we just see that LM just
[46:44] (2804.72s)
empower engineers. So, um, you know,
[46:47] (2807.68s)
it's something that, uh, we use as a
[46:50] (2810.00s)
tool and there's a lot of nonAI tools
[46:52] (2812.08s)
that we use at work that write code for
[46:53] (2813.76s)
us as well and that doesn't scare
[46:55] (2815.68s)
anyone. U, this is just like a little
[46:58] (2818.00s)
bit further. In conclusion, I don't
[47:00] (2820.72s)
think it will majorly change things and
[47:02] (2822.80s)
I wouldn't change your strategy. Like,
[47:05] (2825.04s)
don't just change major from CS cuz
[47:07] (2827.36s)
you're worried that there's not going to
[47:08] (2828.64s)
be any jobs. At least for for the people
[47:11] (2831.84s)
in this room, if you were maybe a
[47:14] (2834.16s)
freshman in high school or something, I
[47:16] (2836.16s)
I'm not sure.
[47:18] (2838.48s)
Yeah. I think uh my perspective on it is
[47:21] (2841.60s)
there are always going to be people who
[47:23] (2843.60s)
have to kind of like tell the AI what to
[47:25] (2845.84s)
do, right? Tell know the AI has got to
[47:28] (2848.16s)
build in this way and then kind of like
[47:30] (2850.16s)
verify that it's like correct. So even
[47:33] (2853.04s)
though even in like 5 10 years in the
[47:35] (2855.28s)
future, I am still imagining right maybe
[47:37] (2857.60s)
like an entry-level software engineer,
[47:39] (2859.36s)
they're not coding as much because the
[47:41] (2861.04s)
AI is going to do it for them, but
[47:42] (2862.56s)
they're still kind of like figuring out
[47:44] (2864.16s)
how to, you know, get that AI to do what
[47:47] (2867.28s)
you want you want it to do. Uh because
[47:49] (2869.84s)
AI is always going to be a tool for us,
[47:51] (2871.36s)
right? And also people got to build the
[47:52] (2872.96s)
AI. Uh so that's kind of my perspective
[47:56] (2876.00s)
on things.