[srvmshr]

I feel an insane hesitation to try for/jump to FAANG. Its not about any ideological reasons: I just feel inadequate, even with a PhD. My research was on applied ML. I feel I haven't 'made the cut' with solving theory or optimization problems in ML. Nor am I in seriously good shape to tackle core CS topics - because that was not the focus during PhD.

What would you advise for someone like me who would want to make the jump in future?

[hedora]

Honestly, you're probably overqualified for an entry-level position at a FAANG. Go to an academic CS conference with FAANG employees, find some that are talking about interesting stuff, and give them your resume.

The resume funnels on websites of most large companies go straight to /dev/null. (University recruitment is another story; those usually get put toward the top of hiring managers' piles.)

[srvmshr]

Thank you for the kind words. But I don't seem to have good SWE knowhow. ML area is moving fast on rollers built by cloud enterprises, so I feel actual CS skills get overlooked by folks like us. I already graduated 2 yrs ago (University of Tokyo - FAANG presence is lesser in Japan). I will be mostly leaning on mailing people I know via PhD/LinkedIn/Twitter.

[coachdarek]

+1 to the comment on the resume funnels being a black hole.

The best option is to directly interface with recruiters. Recruiting is a numbers game at the end of the day. There are pseudo-infinite recruiters and a few applications for each company.

[coachdarek]

Hi I totally understand the sentiment and this is a very common thought that I've seen people have about making the jump to FAANG. Just a heads up, my advice is in the context of getting SWE roles at FAANG companies.

First off, you definitely can make the jump to FAANG. You've gone through a PhD in applied ML. That's really challenging and at least in my opinion much challenging and impressive than getting an entry-level FAANG SWE job. Even if you don't know the core CS topics now, I doubt it will be that hard for you to pick them up. It will take some time to ramp up on them, but not too long.

To give more reference, if you were to go into FAANG with a PhD you would usually start as an SDE2 and earn 300k+ per year as a starting all-in compensation.

If you want to make a go into a FAANG SWE role, then here's what I'd recommend. My guess is the whole process would take ~300 hours so maybe 2-3 months if you do it 40 hours per week or ~5 months at 20 hours per week.

1. Learn to code in Python. You probably know a coding language (if not Python) by doing ML. Otherwise there are tons of free resources to get up to speed on Python (https://introcs.cs.princeton.edu/python/home/ is an example)

2. Data Structures / Algorithms. With your math background this should come quickly. You'll need to learn the bread-butter algorithms, the ones covered in every undergrad course on DS/A. Again there are tons of free resources online. Here's MIT's open source one (https://ocw.mit.edu/courses/6-006-introduction-to-algorithms...)

3. Practicing Interview Problems. Complete all the problems on here - https://neetcode.io/. If you don't understand a problem, study the solutions and google around until you do.

4. Mock Interviews. If you have friends in FAANG ask them to do a few mocks for you - this is what I did. Alternatively you can use pramp.com and interviewing.io to schedule some mocks. If none of these work, can DM me on hackernews and I can try and help set something up.

5. Recruiting / Getting Interviews. I've made several posts on reddit about this (https://www.reddit.com/user/coachdarek) but with a PhD all you need to do is ping 25 recruiters on LinkedIn per day saying you're interested in SWE roles. You'll get at least a few interviews lined up with 2 weeks. PhDs are hot commodities by big-tech. 6. Interview. This is the easy part, once you've got the interview skills and interviews setup, you just show up and do them. Make sure to have at least 6 interviews scheduled because there's a lot of variance in each interview.

If you have more questions, happy to answer and follow ups.

EDITED: To make the bullets on separate lines.

[srvmshr]

Thank you so so much! I guess #3-5 is what I need to work on. Let me go over a plan & if I still need some advice, I will shoot you an email. Thanks genuinely