OpenAI SWE interview guide

L5 is senior — L6 is staff

01Intro

Mission · ownership · why OpenAI

02Screens

Coding + architecture gates

03Onsite

Code · design · behavioral

04L6+

Project presentation more common

05Bar

Mission · safety · judgment

OpenAI ladder from L4 through L6 staff with loop stages. — L5 is senior; L6 is staff — project presentation and multi-behavioral depth are more common at L6+.

Confirm your exact round list with the recruiter. Treat published full menus as a superset.

Common L5 shape (and L6 extras)

Recruiter / HM intro (mission + ownership screen)
Coding screen
Architecture / system design screen
Onsite: coding, system design, behavioral (sometimes ×2), optional domain round
Project presentation more common at L6+

Coding expects production-quality reasoning: how the solution integrates and fails at scale, not only the happy-path algorithm. Mission and safety are not optional flavor — interviewers listen for judgment about misuse, evaluation, and operational responsibility.

Design, mission, and behavioral emphasis

Design rounds probe requirements gathering, tradeoffs, and systems at OpenAI’s operational reality. Behavioral probes collaboration, influence, and judgment under ambiguity — plus whether your values fit the mission. At L6+, expect to present a real project and defend decisions in front of a mixed audience.

Staff bar comparison across Amazon, Google, Meta, and OpenAI. — Staff bar: Amazon LPs+BR, Google 2×SD+HC, Meta 2 designs+retro, OpenAI mission+presentation.

Nearby: Microsoft L63–L64 (deepened)

Microsoft senior loops (roughly L63–L64) are often 4–5 interviews over Teams: coding ×2–3 and system design, each with a pre-announced behavioral focus (often no separate behavioral round until higher levels). An OA or phone screen usually gates the loop. Ask the hiring manager what the team ships — prep relevance helps more here than at brand-only FAANG loops.

Emphasize scoping ambiguous problems without spoon-fed requirements
Design > classic OOD at senior levels; still brush LLD if the team is product/client-heavy
Authentic “why Microsoft / why this product” beats brand worship
Staff-adjacent levels: bring cross-team influence stories even when the loop looks “just coding + design”
Each interviewer’s behavioral theme is announced — map one STAR story per theme in advance

Nearby: Airbnb G9 (deepened)

Airbnb G9 is the senior band. Expect a polished mix of coding, design, and values/culture fit with strong product sense. Marketplace tradeoffs (trust & safety, two-sided liquidity, quality bars) show up in both design and behavioral. Formats shift by org — confirm with your recruiter.

Read recent engineering blogs; cite a concrete system you admire and why
Product sense: host/guest incentives, abuse edges, and measurement
Values fit is scored seriously — rehearse belonging, craft, and feedback stories
Staff-leaning packets: show how you raised quality bars beyond your pod

Staff-aware prep checklist

Confirm level: L5 senior vs L6 staff — ask about project presentation explicitly
Mission brief: Charter + one safety/eval topic you can discuss for 10 minutes
Coding: production-minded mediums — coding path
Architecture screen: requirements, tradeoffs, operational failure — not only boxes
Behavioral ×2: influence, ambiguity, responsibility — behavioral
If L6+: 30–40 min project presentation with metrics, regrets, and research/eng interface
Parallel tracks: Microsoft behavioral themes; Airbnb product + values

Backend focus + mission fit

CodeProd reasoning

Integrate · fail at scale

HLDOps reality

Eval · reliability

BehJudgment

Ambiguity · influence

MissionSafety/eval

Responsibility

L6+Presentation

Defend a real project

Round scripts — first 2 minutes

Say this

Coding: “Here’s how I’ll use the time: clarify constraints (~1 min), state approach + complexity, code, then tests. I’ll narrate as I go — stop me if you want a different path.” Add: “I’ll note how this integrates and fails in a real service.”
LLD (if any): “I’ll confirm actors and use cases, list core entities and public APIs, then design for the hottest concurrency path. I’ll walk one end-to-end scenario before polishing.”
HLD / architecture: “I’ll lock goals/non-goals and scale assumptions, sketch the data path, call out consistency and failure modes, then deepen wherever you care most — storage, API, or ops.” Add: “I’ll call out evaluation/observability and blast-radius controls.”
Behavioral / mission: “I’ll connect a real decision to responsibility and user impact — what I optimized for and what I refused.”
Project presentation (L6+): “Problem → constraints → bets → metrics → failures → what I’d redo — then I’m open for hard questions.”

Lattice study path for OpenAI

Backend prep plan
Coding — production-minded mediums
Core concepts + API design + consistency
Technologies — queues, caches, Postgres/Dynamo as needed
One solid architecture mock from question breakdowns with ops/failure depth
Behavioral + AI ownership questions
Mission brief: charter notes + one safety/eval topic you can discuss 10 minutes
If L6+: 30–40 min project presentation dry-run; this OpenAI guide’s leveling section

Signal rubric — OpenAI strong vs weak

7-day OpenAI plan (week before)

D−7: Confirm level + whether a project presentation is expected.
D−6: Coding mocks with “how this fails in prod” narration.
D−5: Architecture mock — requirements, tradeoffs, operational failure.
D−4: Mission/safety teach-back (10 min) + two behavioral stories.
D−3: Influence/ambiguity STAR stories; skim AI ownership post.
D−2: L6+: full project presentation dry-run; else second design mock.
D−1: Light review; sleep; why-OpenAI in 90 seconds.

01L5 is senior — L6 is staff

02Common L5 shape (and L6 extras)

03Design, mission, and behavioral emphasis

04Nearby: Microsoft L63–L64 (deepened)

05Nearby: Airbnb G9 (deepened)

06Staff-aware prep checklist

07Backend focus + mission fit

08Round scripts — first 2 minutes

09Lattice study path for OpenAI

10Signal rubric — OpenAI strong vs weak

117-day OpenAI plan (week before)