OpenAI Research Engineer Interview Guide
Updated by OpenAI candidates
Our guides are created from recent, real, first-hand insights shared by interviewers and candidates. If your experience differs, tell us here.
OpenAI's research engineer interview goes deeper on ML fundamentals than almost any other big tech loop, with rounds covering distributed systems coding, transformer debugging, and information theory. The depth of statistics can catch some candidates off guard; standard prep won't get you through it.
This guide breaks down each stage of the process, what OpenAI interviewers evaluate, and how to prepare with real example questions and actionable tips.
OpenAI research engineer interview process
The OpenAI research engineer interview is front-loaded with ML and statistics depth; most of the heavy lifting happens before you ever speak to a hiring manager. Expect multiple coding rounds, a statistics-heavy ML round, and a transformer debugging exercise before reaching the final stages.
Before going in, keep in mind that OpenAI interviewers can be less assistive than at other big tech companies, with wide variance in engagement levels between rounds. Don't expect hints; the process is designed for you to work through problems independently.
Here's an example of what the process can look like:
- Recruiter screen: Covers availability, team matching, and your motivation for joining OpenAI
- Technical screen 1: An algorithm design problem at moderate-to-hard difficulty, with extensions added as you progress
- Technical screen 2: A multi-part coding problem where speed and correctness are both evaluated
- Coding and ML stats round: Distributed systems coding intertwined with information theory concepts
- ML debugging round: Find and fix bugs in an existing transformer implementation, then extend it with KV caching
- Coding and design round: Implement a key-value store serializer and deserializer with state management logic
- Hiring manager round: Discuss your experience with cross-team collaboration, leadership, and conflict resolution
- Project deep dive: Describe a past project in detail and explain your design and technical decisions
The OpenAI research engineer interview process can vary significantly by team. The stages outlined here reflect one candidate's experience; the rounds and their sequencing may differ depending on the team you're matched with.
Recruiter screen
The OpenAI recruiter screen covers more ground than a typical availability check. Expect a real conversation about your background, which teams you're interested in, and why you want to join OpenAI specifically.
What the recruiter screen covers:
- Team matching: Your experience and interests are used to place you on the right team before the loop starts
- Availability: Start dates and notice periods come up in more depth than at most companies
- Motivation: Be prepared to discuss why you want to join OpenAI
Unlike a lot of other big tech loops, team placement happens here, before interviews begin. How you present your experience and interests in this call can shape where you land.
Recently asked questions
One candidate reported being asked why they wanted to join OpenAI; this wasn't a checkbox question but a substantive part of the conversation.
Other questions you might see:
Asked at Technical screen 1
The first OpenAI technical screen is a moderate-to-hard algorithm problem delivered in a 60-minute coding session. Expect something that tests your grasp of fundamental algorithm design and requires you to extend your solution as the problem gets harder.
The problem builds in complexity as you progress. A clean first solution isn't enough; interviewers add constraints that require you to rethink your approach.
What interviewers evaluate:
- Algorithm design: Whether you can identify the optimal approach, not just a working one
- Adaptability: How you extend your solution when new constraints are introduced
- Fundamentals: Core CS concepts applied to unfamiliar problem shapes
Recently asked questions
One candidate reported being asked to find the latest Python version that supports a given package. The basic solution was a straightforward binary search, but getting to a fully correct solution required extending it into a hierarchical binary search to handle a broader set of cases.
Other questions you might see:
Asked at Technical screen 2
The second OpenAI technical screen shifts emphasis from algorithm design to execution. The problem itself isn't as complex as the first technical screen. What's being evaluated here is whether you can write correct, clean code quickly across multiple parts.
What interviewers evaluate:
- Speed: Whether you can complete multiple parts within a 60-minute window
- Correctness: Clean, working code matters more than a clever approach
- Incremental thinking: Each part builds on the last; falling behind early compounds quickly
Recently asked questions
One candidate reported a problem that started with a string representing whether an instrument was played on each beat, and asked them to convert it to music notation. The problem expanded in three parts: first for a single instrument, then for multiple instruments combined, then adding support for rests.
Other questions you might see:
Asked at OpenAI • Coding and ML stats round
The coding and ML stats round combines distributed systems coding with information theory concepts at a depth that trips up candidates who prep only on standard coding problems. One candidate described this as the hardest round of the whole loop.
What interviewers evaluate:
- ML systems fluency: Whether you understand distributed operations like all_gather at an implementation level
- Statistics depth: Information theory concepts applied to real systems problems
- Problem decomposition: Breaking an open-ended optimization question into a structured approach
- Communication: Explaining your reasoning through mathematically dense material
Recently asked questions
One candidate reported a multi-part problem centered on implementing an all_gather operation across multiple nodes. The parts progressed as follows:
- Part 1: Implement all_gather across nodes with noisy communication channels
- Part 2: Derive a formula for the number of rounds needed to reach a target accuracy given the channel noise level
- Part 3: Improve on that bound; the only prompt given was that the naive upper bound was too high and too slow. Figuring out that float precision could be exploited to design a better algorithm was left to the candidate.
Other questions you might encounter:
Asked at OpenAI • 
Asked at Anthropic • To prepare for this round, a candidate recommended taking a graduate-level Introduction to Information Theory course. Any rigorous graduate-level course covering the topic will address the concepts tested here.
ML debugging round
The ML debugging round gives you an existing architecture such as a model or transformer implementation and asks you to find bugs. The buggy sections are pre-annotated so you’ll know where to look. Once you've found and fixed the bugs, the round expands into an open-ended set of tasks, like implementing KV caching.
What interviewers evaluate:
- Transformer internals: Whether you understand how a transformer implementation should behave at a code level
- Debugging methodology: How you isolate and reason through bugs in unfamiliar code
- ML systems depth: Whether you can extend a transformer implementation without being told exactly what to change
- Open-ended problem solving: KV caching has multiple valid implementation paths; interviewers want to see how far you take it
Recently asked questions
One candidate reported being given a ~300-line transformer implementation with four annotated sections, each containing a bug. After identifying and fixing all four, they were asked to implement KV caching.
Other questions you might see:
Asked at Sierra AI • Coding and system design round
The coding and system design round is more design-heavy than a standard coding challenge. You're asked to implement a working solution, but the design decisions around data structure and state management are a core part of what's being evaluated.
What interviewers evaluate:
- System design thinking: How you structure a solution before writing code
- Implementation correctness: A working serializer and deserializer, not just a sketch
- State management: Whether you can handle storing and restoring system state correctly
- Edge case reasoning: What happens when state is queried while the system is offline
Recently asked questions
One candidate reported being asked to implement a key-value store serializer and deserializer. The problem extended beyond basic serialization; the data structure needed to store the state of a system and restore it correctly on demand. Querying state while the system was shut down required a specific output, adding a layer of design complexity on top of the implementation.
Other questions you might see:
Hiring manager round
The hiring manager round is a behavioral interview that focuses on collaboration, communication, and your perspective on AI. Expect questions about how you've navigated conflict, competing priorities, and working across teams.
What interviewers evaluate:
- Collaboration: How you've handled disagreements, differing opinions, and competing goals with stakeholders
- Communication: Your ability to explain technical concepts clearly and work effectively with non-technical stakeholders
- AI perspective: Whether you've engaged with AI beyond the technical aspects and have a considered view on where the field is heading
Recently asked questions
Here are real interview questions reported by candidates:
Asked at Past project round
The past project round is a 60-minute presentation where you walk interviewers through a complex project you've worked on. OpenAI's emphasis on scale means projects that reach large numbers of users tend to land better than ones defined primarily by technical complexity alone.
Prepare a slide deck and expect follow-up questions throughout. Come ready to explain why you made the decisions you did; interviewers want to understand your reasoning, not just the outcome.
What interviewers evaluate:
- Scale: Whether you understand the technical and infrastructural demands of systems with large user bases
- Decision-making: Why you chose specific tools, frameworks, AI processes, or approaches over alternatives
- Leadership: How you defended your approach, secured buy-in, and communicated with stakeholders
Recently asked questions
Here are some examples of questions reported by candidates:
Asked at How to prepare for the OpenAI research engineer interview
The OpenAI research engineer interview tests your understanding of coding fundamentals over pattern recognition. Grinding coding questions won't move the needle here; the problems are practical, the extensions are open-ended, and the statistics depth requires real subject matter expertise.
- Study information theory at the graduate level: The ML stats round tests concepts that require genuine fluency, not surface-level familiarity. One candidate specifically recommended taking a graduate-level Introduction to Information Theory course.
- Practice practical coding problems: CodeSignal-style exercises that ask you to design a system and add complexity incrementally are closer to what you'll face. Speed and correctness both matter; being a clean, fast coder is a specific requirement in at least one round.
- Know your AI architecture: The ML debugging round assumes you understand transformer implementation, neural net training, and other AI fundamentals.
- Get comfortable with distributed systems operations: All_gather and similar distributed ops appeared in the ML stats round as the coding vehicle for statistics problems. Understanding these at an implementation level, not just conceptually, is important.
About the OpenAI research engineer role
The OpenAI research engineer role sits at the intersection of engineering and research. It's distinct from a standard software engineer role; research engineers have more room to contribute to or pursue research directly, alongside their engineering responsibilities.
What OpenAI research engineers do and who thrives in the role
- Research and engineering blend: Research engineers contribute to both applied engineering work and research, with the balance depending on the team
- Model training and optimization: End-to-end ownership of model development, pre- and post-training, and efficiency optimization
- ML systems work: Building, debugging, and optimizing ML systems, including large-scale transformer implementations and distributed training infrastructure
- Collaboration and leadership: Working closely with researchers and other engineers on problems that don't always have a defined solution path
Candidates who thrive tend to have strong ML systems fundamentals, genuine research curiosity, and the ability to work through open-ended problems without much external scaffolding. As a senior role, OpenAI research engineers are expected to act in a leadership capacity and to help explain the concepts behind their work.
OpenAI research engineer experience and education requirements
OpenAI expects strong coding ability, deep ML systems knowledge, and graduate-level statistics fluency. One candidate interviewed at what they estimated to be a senior-equivalent level, though this wasn't confirmed by OpenAI directly.
Additional resources
- OpenAI Careers Page
- OpenAI Interview Guide
- Data Science Interview Guide
- Generative AI Interview Guide
- OpenAI Interview Questions
FAQs about the OpenAI research engineer interview
How many rounds are in the OpenAI research engineer interview?
The OpenAI research engineer interview process outlined in this guide includes these confirmed rounds:
- A recruiter screen
- Two technical screens
- A coding and ML stats round
- An ML debugging round
- A coding and system design round
- A hiring manager interview
- A deep dive into a past project
The total number of rounds may also vary by team.
What's the hardest part of the OpenAI research engineer interview?
A recent candidate said that the coding and ML stats round was the hardest part of the OpenAI research engineer interview loop. It requires genuine fluency in information theory concepts applied to distributed systems problems; one candidate described it as the round most likely to catch people off guard if they've only prepped on standard coding problems.
How long is the OpenAI Research Engineer interview process?
The timeline changes depending on the availability of different interviewers, but the process takes between two weeks and two months.
Do OpenAI interviewers give hints or guidance during rounds?
OpenAI interviewers are less assistive than at most big tech companies. One candidate noted wider variance in engagement levels between interviewers than they'd experienced elsewhere. Don't count on hints to get unstuck; the rounds are designed for you to work through problems independently.
How much do OpenAI Research Engineers make?
According to Levels.fyi, OpenAI Research engineers make the following in total compensation:
- L4: $763,000
- L5: $1.36 M
Learn everything you need to ace your research engineer interviews.
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