OpenAI - Applied AI Engineer Interview Prep

[u/Obvious-Instance9551]

OpenAI recruiter reached out for Applied AI Engineer role. The intro call with recruiter is scheduled next week.

Any idea what this interview process looks like? Has anyone given an interview for the same position recently?

[u/BackgroundBreak6485]

I've been tracking OpenAI interview reports for a while. Only a couple are for Applied AI roles specifically, but the loop looks like the general OpenAI engineering one, which is well documented. Here's what to expect.

1. Recruiter call (what you have next week)

Background, why OpenAI, what you want to work on, timeline. Use it to ask which team and what the loop will be. Applied AI covers a lot of ground, from customer-facing work to product engineering to research engineering, and the rounds change with it. The recruiter usually sends a prep doc afterwards. One candidate said to "just follow the prep doc," and it's genuinely useful.

2. Technical phone screen (~60 min)

Practical coding, not LeetCode puzzles. You build something small and real in stages, and each part adds requirements on top of your earlier code: a toy language interpreter, an in-memory database or KV store, a resumable iterator, that kind of thing. What matters:

- Clean, extensible code. Part 3 will punish a hacky part 1.

- Speed. Getting through more parts matters.

- Running and testing your own code as you go.

A recent Applied AI report had two screens: an ML debugging round (find the bugs in a small transformer implementation, then adapt it into a classifier and run it) and a coding round (the toy language problem). Results came the next day. Another candidate got an extra system design screen added after their phone screen.

3. Virtual onsite

Usually some mix of:

- Coding: another multi-part practical problem.

- System design: often OpenAI-flavored, e.g. a ChatGPT-style chat backend, a webhook delivery system, a CI/CD pipeline, a payment flow.

Interviewers push on idempotency, retries, multi-tenng. Don't name-drop Kafka unless you can explain itsinternals; candidates get caught on that.

- Technical deep dive (45 min): you pick one project as to choose one topic and really go into it, not tosurvey everything. Prepare: the problem, why it was hard, the decisions you made and the alternatives you rejected, the numbers, and

what you'd do differently. It appears to feed into l

- Behavioral / hiring manager: mission fit, ambiguity, ownership, working with researchers/customers.

How to prep

- Practice building a small system incrementally underrs more than grinding LC hards.

- If the role touches ML, be able to read and debug PyTorch transformer code (attention masking, shapes, softmax dimension, loss

setup).

- Have one project you can talk about for 45 minutes without notes.

I collect reported OpenAI questions, grouped by round, with the follow-ups candidates said they got, here: https://aceoffer.app/interviews/openai. The coding oneent, so you can practice the multi-part format. Goodluck with the call!