Working at OpenAI means building for the unexpected. From serving powerful models to millions of users to preventing subtle misuse at the API layer, engineering here is deeply intertwined with research, safety, and product development. OpenAI’s interviews are designed to reveal how you reason through complex systems, prioritize correctness under uncertainty, and scale infrastructure that must behave responsibly in the wild.
OpenAI interview structure
Team fit and recruiter screen
The first step is a conversation to understand your background and determine where you’d contribute best, whether that’s scaling infrastructure, superalignment, DevTools, or Applied AI. Roles vary widely across infrastructure, research engineering, and product teams.
Use this call to:
- Clarify the technical maturity of the team you’d join.
- Understand how much System Design or ML depth is expected.
- Learn what a successful engineer looks like in that part of the organization.
Initial coding interviews
These one-on-one technical sessions focus on how you solve problems under tight correctness, and resource constraints.
Example problem domains:
- Designing APIs that safely handle untrusted input.
- Building token-aware data pipelines that serve LLMs.
- Implementing concurrency-safe, observable components.
You’ll want to:
- Justify trade-offs (latency vs. robustness, speed vs. safety).
- Think through how your code behaves under scale or attack.
- Communicate edge cases before they trip you up during implementation.
Final round: The onsite loop
This consists of 4–6 interviews across technical depth, collaboration, and OpenAI-specific engineering values.
Deep dive coding
These sessions go beyond syntax:
- Solve moderately complex algorithmic or data transformation problems.
- Use or build lightweight abstractions with strong guardrails.
- Apply reasoning that anticipates misuse, overload, or abuse.
Systems conversations
Expect architecture discussions that touch real OpenAI infrastructure themes:
- Serving low-latency inference at global scale.
- Designing plugin evaluation sandboxes for third-party extensions.
- Modeling failure-resistant routing across clusters and GPUs.
Your approach should:
- Surface edge cases clearly.
- Emphasize observability, rate-limiting, and fallback paths.
- Balance research agility with platform resilience.
Cross-functional judgment
You may be asked to:
- Debug a prompt-injection scenario.
- Walk through an incident postmortem from a degraded rollout.
- Discuss when you’ve shipped with incomplete information and why.
Interviewers are looking for:
- Nuanced decision-making in high-trust environments.
- An understanding of safety, compliance, or ethical constraints.
- Curiosity matched by self-discipline in execution.
What OpenAI values in engineers
The best engineers here operate with high agency, but also pause before making assumptions. They work at the intersection of safety, research, and usability, knowing that the smallest regression in behavior can affect millions.
You might:
- Build tooling that lets policy teams evaluate model outputs more easily.
- Spot a rare failure mode in the inference pipeline and make it testable.
- Refactor rate-limiting logic to allow better customization without breaking safety rules.
- Collaborate across alignment researchers, infra engineers, and security reviewers in one design session.
It’s a place where great code is only the beginning: how it behaves when no one is looking matters more.
Getting ready for your OpenAI interview
What sets candidates apart:
- Systems fluency: understanding memory, network, and failure behavior deeply.
- Clarity under ambiguity, especially around incomplete requirements or moving safety targets.
- A bias toward building tools, not just solutions.
- An instinct for simplifying complexity without stripping away nuance.
If you want to write software that helps shape how the world interacts with intelligence, and ensure that interaction remains responsible, OpenAI’s interview process will challenge you. It will test how deeply you can think and, most importantly, how clearly you can build.