Home Archive Trilium Notes About
Home Archive Trilium Notes About

How I got to OpenAI

Posted on 2023-01-11

In May 2022, I started working at OpenAI as a resident on RL team, and eventually I got hired as full-time. As of today, I’m still at OpenAI, and I’m working on some ChatGPT related stuff and try to make progress on scalable alignment. I want to make a brief write-up of how this happened, because sometimes people ask about it and it might be helpful to other people in a similar situation.

TL;DR, this is the last couple steps:

My longer relevant history follows.

My history

Before university

Rai sticker, by ParelDraakje

I’ve always been good with math and computers. (I started out as a more general hacker, including electronics etc., but eventually specialized.)

I did some competitive programming, high school programming seminars, olympiads etc. I got to district level rounds, and my top achievements were being ~#20-30 in competitive “algorithmic programming” (which is part of math olympiad in Czech Republic), and being #1 in competitive “practical programming” (which is more “write a program for task X” rather than “solve this theoretical problem”.


I took a wide approach to consume all the knowledge at uni. Since high school I’ve been freelancing, writing up small programs/websites. I leveraged that into a series of internships - first Represent.com in Prague (via Juraj Masar, who was in roughly the same uni year), then half a year for Google in Paris, then Dropbox.

When in USA on the Dropbox internship, I got an offer from Cruise and Coinbase via Triplebyte. I also did full-time interviews with Google and got an offer.

It turned out I was not mentally/spiritually ready for making the move to USA. I continued on with a master’s, keeping the Google offer in my back pocket.


It was easy to do all the required courses but I ended up getting stuck on the last master’s requirement - the thesis. I tried to work on knowledge base completion with Pasky as my supervisor (who ended up (co-?)foundinG Rossum where he’s currently CTO). This was roughly at the time when the seminal Transformers paper Attention is All You Need, maybe +/- half a year or so.

I got involved in the rationality community in Prague and the EA community that eventually sprouted out of it. I co-founded the Czech EA association.

I wanted to do an open-source re-implementation of Google’s Knowledge Vault. It’s basically where you take Google’s Knowledge Graph, you mix in a bunch of unstructured data like Wikipedia or such, and based on that and some neural networks trained on the graph, you make a bigger graph, where you suggest “I think there’s relation R between entities X and Y, with probability P=0.97”.

Coming from Google, I assumed this would be pretty easy - you’d plug in standard building blocks for MapReduce-type pipelines, and out would come a model. I was trying to immediately jump to a larger scale project - completing the DBpedia knowledge graph with Wikipedia text.

Things turned out way harder than I expected. I tried to use Bazel to build Hadoop stuff to chew up Wikipedia articles, and HBase (Hadoop’s BigTable basically) to store Wikipedia articles. I ran into tons of stupid problems - like “oops, library X which I need and library Y which I need depend on versions of library Z which are incompatible, so you gotta split these 2 functions into separate binaries”. Or “oops maximum runtime on this cluster is 24 hours lol”. The iteration time was horrible.

Rai sticker, by Ketzel99.
It doesn’t have a name but I think “computers were a mistake” would fit.

Today I’d probably try to approach this with less hubris, and try to start from smaller pieces, so that I’d have a fast iteration turn-out. And also, I’d just … drop all the manual NLP and throw a Transformer at the problem. But oh well, you live and learn.

I was working in the same office as a group of CUNI folks who ended up winning an Amazon tournament for writing a chit-chat agent, but I wasn’t working in their ecosystem - the knowledge base completion I was doing was basically playing on my own little field.

On top of that, I experienced a mental health crisis, and I felt like neither me nor anyone else will ever care about what I do in the thesis. I became depressed and to this day feel like I haven’t quite 100% recovered. But I think that’s more “pre-existing problems came to the surface and I can no longer ignore them”, rather than “at this point I started having problems”. But I always felt sadness/envy/impostor syndrome seeing that I wasn’t one of the national-best-level people in the theoretical stuff.

In between the work being hard and frustrating and disconnected, and the mental health crisis, I dropped my master’s thesis, but stayed formally enrolled as a student.

“ded x.x” Rai sticker, by ParelDraakje

Google ended up getting tired of me trying to postpone the offer for longer. I accepted it, didn’t get a H1b, and ended up getting an offer in Google Switzerland.


At Google, the first team I worked on was doing some stuff which cared about metrics and was downstream of NLP, but there wasn’t really organizational alignment that AI mattered. There were some applications of AI in isolated places, but the problems my team was responsible for were much more shaped like software engineering - building pipelines, services, etc. - than research engineering.

I was sorta hoping to finish my master’s thesis, but of course, didn’t have time to do it. I kept paying my uni to extend my studies, but eventually the clock ran out. And so I did finish all the courses for a master’s, but never actually got it, because I didn’t get around to doing the thesis.

Within Google, my original hope was that I’d try to position myself into a team that would be AI or AI-adjacent, do researchy stuff, eventually position myself to work on AI safety. But it ended up very hard trying to switch myself from “software engineer working in team X doing software engineer things” into “research engineer in ML”. I didn’t have any proven experience saying “I can do ML” - all I had was “hey I did all this stuff in uni”. But whenever I tried to apply for an internal transfer, somehow it didn’t end up working out. I think there must have been very hard competition. And getting rejections was always an ow.

My self-confidence suffered and I started feeling like I’m “not smart enough” or “not good enough to do this”. I envied people I knew who were working with ML.

Eventually I ended up saving up a bunch of money, including being lucky with crypto. I though about it a bunch, and decided that I just felt dissatisfied with the work at Google. It wasn’t evolving me in the direction of working in AI. I was good at my work, but I wasn’t getting the skills I wanted to get.

FIRE mode

Since uni I was following the “financial independence / early retirement” (FIRE) philosophy. See places like /r/financialindependence for info on that. With the money I saved my simulations showed I had a maybe like 80% of being able to stay in Switzerland, on my level of spending, basically indefinitely.

So I started just chilling in Switzerland, basically trying out “what early retirement would be like”.

I played a lot of Civilization 6 and Civilization 5. I didn’t really feel better than when I was working - maybe even worse. When you’re at work, you automatically get a bit of socializing. When you’re not, it’s actually up to your own initiative to meet people, and that was sorta hard in Switzerland.

Chilling and learning ML

I never thought of FIRE as “I’d retire and then I just chill on the beach sipping piña coladas forever”. I wanted to find a mix of seeing friends, having fun, and doing stuff that I felt like it mattered.

When I did the EA stuff in Czech Republic (like organizing the first EAGxPrague), it felt like it mattered. Work on AI alignment would matter - if I could manage to do it. Failing that, I thought maybe I’d find work someplace where I liked the mission and product. I contributed to some open-source, like Anki and Athens Research.

I’ve decided to try to put some more effort re-learning all the stuff I learned about AI in uni, except this time I wanted to actually grok it, where you could wake me up at 2 AM 5 years from now and I’d still be able to explain to you how it works. I went over old materials and re-learned them, making Anki cards, and started refilling the holes where my knowledge was stuck before 2017-era progress in AI - like deep RL or Transformer language models.

Also a Rai sticker by Ketzel99.
“Science was a mistake”? Maybe writing was? When reading paper X that assumes you’ve taken Advanced Xology and did all the background reading on Y, Z, α, β… sometimes it indeed do be like that.

To repeat the bullet points from the initial TL;DR, this was the procedure:

Nowadays, I’d actually recommend Jacob Hilton’s Deep Learning curriculum. It’s based on an OpenAI-internal curriculum for residents and it’s really good at building up a good background for work at OpenAI.

So, I’ve been slowly taking courses, sometimes experimenting, sometimes reading papers. Eventually I found the paper Proximal Policy Optimization Algorithms.

Rai “STACK MORE LAYERS” sticker, by Ketzel99.
This is my life now.

I tried pretty hard to grok it, because it is sorta magic. One day I wanna write a blog post explaining it. There’s a whole bunch of math involved in proving that it actually works. But I’ve been trying to build really deep foundations, really grok all the stuff involved in doing ML. To the point that theoretically I’d be able to relay all of this to a Russian babushka, given enough time.

Reading that paper, I found a few small mistakes in exposition. The first author on that paper is John Schulman. I sent him an email.

In the background I’ve been on the lookout for some roles. I hoped eventually I’d be able to take some role where I’d build up a bit of ML experience, and then I’d be able to take that and leverage it into a role closer to AI safety.

Places I’ve been looking at included:

John responded to the email, and invited me to apply to the OpenAI residency.

OpenAI had 2 residency tracks: software engineer and research. Originally I was scared of applying for research, because of all the self-doubt and impostor syndrome I was (and still to a degree am) carrying from having failed to write a master’s thesis.

But John encouraged me to try the research track, maybe believing in myself more than I did - and I made it through. The research interview was the most nervous I felt interviewing ever - it was a multi-hour small sized research project involving tools like Jupyter, NumPy, etc.

And I made it. It was very useful to have all the commands for Pandas, NumPy, etc. memorized in Anki. But if I had to take the interview again, I’d recommend myself to spend more time playing around with various algorithms, grokking how they behave as you change parameters, etc.

I ended up getting accepted both for the OpenAI residency, and an internship at CHAI. Both of them would have been awesome to work on. Out of these two, I chose to go with the residency - mostly because I felt that because CHAI was an academic institution, it would have been harder to grow there post-internship if I wasn’t a PhD candidate.

Conclusions and recommendations

Here’s stuff I would have done differently and that I’d recommend to people in a similar position:

I hope maybe this might give another nudge to people who’d like to work on alignment, or that some of this advice might be useful.