OpenAI · Primly Community

OpenAI machine learning engineer interview, what they actually tested beyond the coding round

ml_mike · 5 replies

Went through the MLE loop about two months ago. I've done a lot of these at various labs and OpenAI's is distinct. Not in a bad way, just different enough that generic MLE prep may not be enough.

High-level: 4-5 technical rounds plus behavioral. The mix shifts based on team, but for the team I was targeting (training infra adjacent) it was: coding (2 rounds), ML fundamentals deep-dive, systems design with ML flavor, and a research understanding round.

Coding: Don't underestimate this even if you have strong ML credentials. They do standard algorithmic coding. Two mediums in each round, looking for clean code and good problem-solving. Brush up on trees, graphs, sliding window. The usual.

ML fundamentals: This is the one that separates OpenAI from most MLE loops. They go deep. Transformer architecture internals, attention mechanisms, how you'd explain specific failure modes, training instability and what causes it, loss landscape intuition. If your ML knowledge is mostly from using HuggingFace APIs and calling it a day, you'll struggle here. They want you to actually understand what's happening.

Systems design (ML flavor): For my round it was design a model serving system at scale. Latency vs throughput tradeoffs, batching strategies, quantization, how you'd handle distribution shift in production. Very practical.

Research understanding: I wasn't expecting this but they asked me to walk through a recent paper I found interesting, explain the core contribution, and discuss limitations. Pick something you genuinely understand, not something that sounds impressive. They probed deeper than I expected.

Values: This matters. Be ready to talk about AI safety substantively. Not as a performance, as a real conversation.

I get that some people think OpenAI's stated safety mission is in tension with their pace of deployment. I had a nuanced view on this and shared it. They seemed fine with honest takes.

Comp: for senior MLE in SF the numbers I've seen are very competitive. RSUs or phantom equity depending on structure. Do your homework on how OpenAI equity actually works before you negotiate.

5 replies

pivot_pat

The research paper round is interesting. Did they pick the paper or did you bring your own? And how much time did you have to prepare it in advance?

ml_mike

I brought my own. They told me a few days before to be ready to discuss a paper I found meaningful from the past year or so. I picked something on mechanistic interpretability because that's actually what I find interesting. I'd avoid picking a GPT-4 or similar OpenAI paper just to seem like you did your homework on them.

hardware_hugo

The transformer internals question is a real bar. I've seen people with solid ML engineering track records completely blank on attention head mechanics when asked to go deeper than 'softmax over QK^T'. Study your architecture internals if you haven't in a while.

infra_ines

The model serving system design question is similar to what I got in my platform eng round at a different AI lab. The batching strategies question specifically. It's a good prep target across the AI company tier.

sec_sasha

Curious about team matching. Did you know which team you were targeting before the onsite, or was it post-offer team selection?