Before I get into it: this is a writeup of my full process interviewing for a Full-Stack role at OpenAI, along with my impressions, as a reference for people prepping. OpenAI's interviews are pretty different from traditional big-tech interviews (especially the pure-LeetCode-grinding kind) — it leans a lot more toward "real engineering + product sense + mission fit," so if you only prepped LeetCode, you might feel a little out of your depth. Everything below is just for reference, the exact process will vary a bit by team and by recruiter.
Background & Role
Role: Full-Stack Software Engineer (Applied / Product direction, not pure Research Infra)
Tech stack expectations: frontend leans React / TypeScript, backend is mostly Python, they test API design, real-time interactions, and experience working with LLMs
Interview format: fully virtual (video + a shared editor / your own environment)
Overall pace: from the recruiter's first contact to the offer, about 3-4 weeks, quite a bit faster than a lot of traditional big companies
Overall Process & Timeline
Recruiter Screen (HR call, about 30 min)
Technical phone screen (about 60 min, coding)
Virtual Onsite Loop (usually 4-5 rounds, might be split across one or two days)
- Coding (practical, not pure algorithms)
- Full-Stack hands-on / take-home-style live build
- System Design
- Behavioral / Mission & Values
Team Match + Offer
Let me go through each round below.
Round-by-Round Details
Round 0: Recruiter Screen
Standard opener, the recruiter was pretty nice. What we talked about:
- A quick pass over my resume and recent projects
- Why I wanted to join OpenAI (they genuinely ask this, and pretty much every round after this might ask it again too, so really think it through)
- What direction I wanted (product-facing vs. more infra-leaning)
- Salary expectations, timeline, whether I was interviewing anywhere else
Tip: this round is not just a formality. How you talk about the mission, and how clear you are about direction, goes into the feedback. Don't just say "I think AI is cool" — it's best to tie it to something you've actually built.
Round 1: Technical Phone Screen
Format: shared online editor (similar to CoderPad), you can run code
Question style: not the kind of convoluted hard DP problem — more of a "real engineering" style question. They give you a semi-realistic scenario and have you implement a feature, then keep piling on requirements (lots of follow-ups)
What I got was something like "parse and process a structured data stream" — implement a basic version first, then add edge cases, then they ask about complexity, then ask what you'd do if the data volume were huge / if it were streaming
What they're actually testing:
- Whether your code is clean, whether your naming is clear
- Whether you can think of edge cases yourself
- Communication: talking through your thinking as you write, the interviewer cares a lot about this
- Whether you know the common features of the language/libraries (they don't like it when you reinvent the wheel)
Tip: you're allowed to look things up, allowed to say "I don't remember the exact signature of this API." They're evaluating engineering ability, not API memorization.
Round 2: Full-Stack Hands-On (the most "OpenAI" round)
This is the round that's most different from a traditional interview, and it also carries a lot of weight.
Format: they give you a small requirement and have you build a working small app from scratch, or add a feature / fix a bug on top of an existing small repo
My question: build a simple frontend/backend tool — frontend in React to display things, backend provides the API, and in between you have to handle state and a bit of real-time updating. Some people got something like "hook up an LLM API and build a small demo."
You can use your own environment, you can look things up, and some teams even let you use AI tools (they use them internally anyway, the point is to see how you use them and whether you can get the work done well).
What they're actually looking at:
- Whether you can quickly produce something that actually works (shipping ability > perfect architecture)
- How complete the frontend/backend integration is — did you handle loading / error / empty states
- Code organization: file structure, component breakdown, whether the API design is reasonable
- Your debugging ability: how you locate a problem when something breaks
- Trade-offs: when you're short on time, what you prioritize and what you cut, and being able to explain why
Tip: don't over-engineer right out of the gate. Get the smallest working loop running first, then add robustness incrementally. You have to narrate your trade-offs out loud along the way. OpenAI really values people who are "pragmatic, get things shipped."
Round 3: System Design
More product-oriented system design, not the pure "design a distributed storage system" theory-question kind
What I got was something like "design a real-time/streaming-response AI product feature for a large number of users," and you had to consider:
- API design (especially streaming responses, e.g. SSE / websockets)
- Rate limiting, queuing (this matters a lot when dealing with LLMs)
- Caching strategy, cost control (token / compute cost is something they genuinely care about)
- How the frontend gracefully handles streaming responses and errors
- Observability (monitoring / logging / how to debug when something goes wrong)
Tip: if you have experience with LLMs / high concurrency / real-time systems, definitely bring it up. They like hearing about pitfalls you've actually hit, not reciting a template. Don't forget to proactively bring up trade-offs and "what if this needs to scale 10x."
Round 4: Behavioral / Mission & Values
Do not treat this round as a throwaway — OpenAI takes it quite seriously.
Common questions:
- Talk about a project you drove yourself and actually shipped
- A time you had a technical disagreement with someone, how you handled it
- How you see AI's impact / why you want to be part of this (why OpenAI specifically, not just any random AI company)
- The hardest technical/collaboration problem you've solved, how you solved it
- How you balance "move fast" against "quality/safety" (this question is very OpenAI)
Tip: use STAR, but focus on your personal contribution and the result/impact. Your answer about the mission should be sincere, specific, and reflect your own thinking — don't just chant slogans. They really value ownership, high agency, and people who can find their own direction in ambiguity.
What Each Round Is Really Testing (one line each)
- Phone Screen: clean code + communication + spotting your own edge cases
- Full-Stack Hands-On: quickly ship a complete working thing + trade-offs
- System Design: real-time/streaming + cost + rate limiting + observability
- Behavioral: ownership + mission fit + high agency
Prep Advice
Don't only grind LeetCode. You need the algorithm fundamentals, but put your focus on "real engineering problems + quickly prototyping." Find a few small requirements and practice building a working frontend/backend app from scratch in 30-60 minutes.
Practice streaming / real-time interaction. SSE, WebSocket, how the frontend handles streaming responses — these come up in almost every LLM-adjacent role.
Have a set of experience working with LLMs ready (even if it's just a side project). Rate limiting, cost, prompt/response handling, error retries — these all score points.
Think through "why OpenAI" and be able to tie it to things you've actually done.
Practice talking while you write. Communication is weighted in every single round.
During the interview, feel free to look things up and admit what you're not sure about — this doesn't cost you points, it actually makes you look more genuine and more like an actual engineer.
Some Personal Impressions
The overall experience was more pleasant than traditional big tech — the questions felt closer to real work, not as much of a "testing for the sake of testing" feeling.
The pace was fast, the recruiter communication was efficient.
They're genuinely looking for people who can take something from idea to shipped, on their own, not just people who are good at solving problems. If you're someone who likes building and shipping things day to day, you'll probably vibe with it.
The intensity isn't low — the Full-Stack hands-on round in particular is draining, manage your time carefully.
Good luck with your interviews. Hope you land the offer you want.
Discussion
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