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OpenAI L5 (Staff) Software Engineer Interview Guide
A comprehensive guide to the OpenAI L5 interview process
The OpenAI staff software engineer interview process starts with a recruiter or hiring manager screen, followed by two technical screens (one coding, one architecture/system design), then moves to a final onsite loop with 4-6 rounds covering coding, system design, technical project presentation, and multiple behavioral interviews focused on leadership and collaboration. The entire process typically takes 8-12 weeks from start to finish, though it can stretch to 4+ months due to scheduling delays and the additional calibration required at the staff level.
Staff candidates get evaluated extremely rigorously across technical depth, leadership capability, and strategic thinking. The architecture/system design rounds carry enormous weight since they directly assess your ability to drive requirements gathering, explore complex trade-offs, and design systems at OpenAI's scale. The behavioral interviews are split into leadership and collaboration focus areas, evaluating not just mission alignment but your ability to influence decisions and drive technical strategy. The technical project presentation is mandatory and heavily weighted, as staff engineers must demonstrate their ability to lead major technical initiatives and communicate complex decisions to diverse stakeholders. The coding rounds expect you to write production-quality code while also showing architectural thinking about how your solutions would scale and integrate into larger systems.
The interview consists of 6-8 total rounds:
- Recruiter / Hiring Manager Intro Call
- Coding Screen
- Architecture / System Design Screen
- Onsite (Usually virtual)
- Coding Round
- System Design Round
- Technical Project Presentation
- Behavioral Interview — Leadership
- Behavioral Interview — Collaboration
- (Optional) Domain-Specific Interview
Interview Rounds
Recruiter / Hiring Manager Intro Call
Your first conversation is a comprehensive 30-minute call with your recruiter or hiring manager. This isn't technical, but it carries significantly more weight at the staff level as they're evaluating your potential for organizational impact and technical leadership before investing in the extensive interview process.
The conversation covers your background with a focus on leadership experience, technical depth, and how your trajectory aligns with OpenAI's strategic technical needs. You'll definitely get asked why you want to work at OpenAI specifically, but at the staff level they're looking for evidence that you understand the broader implications of their work and can contribute to their technical strategy. They want to see that you've thought deeply about AI safety, responsible development, and the unique challenges of building AGI.
This round carries enormous weight as an initial screen. Beyond mission alignment, they're evaluating your communication skills and ability to articulate complex technical concepts to diverse audiences - a critical skill for staff engineers who regularly interface with researchers, executives, and external stakeholders.
Spend time reading OpenAI's Charter and recent blog posts about AI safety before this call. Being able to reference specific aspects of their work or values shows you've done your homework and aren't just applying everywhere.
The person you're talking to will also outline the rest of the process and answer any big-picture questions about the role or team. This is your chance to understand what comes next: the coding screen followed by the system design screen, then the onsite rounds.
Don't treat this as a throwaway conversation just because it's not technical. The recruiter or hiring manager's feedback carries real weight in the process, and a lukewarm recommendation here can hurt your chances even if you nail the technical rounds later.
Come with good questions about team dynamics, the specific projects you'd work on, or how OpenAI tackles the technical challenges you care about. The goal is having a real conversation about mutual fit, not just checking boxes.
Coding Screen
After that first call, you'll hit the first technical round: a 60-minute live coding session that sets the tone for everything else. This usually happens over CoderPad, though some candidates get a HackerRank-style thing instead. Either way, you're writing code while an interviewer watches and asks questions in real time.
The problems feel more practical than typical LeetCode grinding. You'll get questions about time-indexed data handling, implementing iterators with state, or building simple caching mechanisms. The concurrency and OOP design elements make this different from pure algorithmic puzzles. They want to see how you think about real software problems that could actually come up at OpenAI.
Most sessions have 1-2 problems, and the interviewer will watch your pace to decide whether to go deeper on one complex challenge or move through multiple scenarios. You can pick any programming language you're comfortable with, which is nice flexibility that not every company gives you.
"The coding problem question was rather long, use the interviewer to focus on important parts."
— Recent OpenAI Staff candidate
Practice coding in shared editors like CoderPad or Repl.it beforehand. The experience of thinking out loud while typing in an unfamiliar environment takes some getting used to, and you don't want your first time to be during the actual interview.
You're evaluated on four main aspects:
- Problem-solving & coding skills: How you break down the problem and implement a working solution
- Code quality & maintainability: Clean, readable code that looks like it could go into production
- Speed & correctness under pressure: Reasonable pace while handling edge cases within the time limit
- Communication & approach clarity: Explaining your thought process and responding well to hints
The no-reference policy means you're coding from memory, so brush up on your language's standard library and common patterns beforehand. Testing your solutions thoroughly matters. Interviewers like when you walk through edge cases or explain how you'd validate the code works.
Don't jump straight into coding. Take a minute to clarify requirements and think through your approach out loud. The interviewer wants to see your problem-solving process, not just the final solution.
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Most commonly asked Coding questions
OpenAI
Staff
Architecture / System Design Screen
The format is designing a complete large-scale system from scratch using a shared online whiteboard, with an emphasis on driving the requirements gathering process. You might be asked to architect something like a distributed ML training platform, real-time model serving infrastructure, or global content distribution system. These problems directly mirror the massive-scale challenges OpenAI faces internally. At the staff level, you're expected to guide the conversation, proactively exploring trade-offs, and demonstrating deep technical judgment about system architecture decisions.
As a staff candidate, you need to drive requirements gathering from the start and operate seamlessly across multiple levels of abstraction. You should begin by clarifying scale requirements, consistency models, and failure scenarios before diving into design. The interviewer will test your technical depth and leadership by exploring complex architectural decisions: why you chose event sourcing over traditional state management, how you'd orchestrate distributed training across thousands of GPUs, or how you'd design for regulatory compliance across multiple regions.
Spend the first 5-10 minutes clarifying requirements and constraints before drawing anything. Ask about expected scale, read/write patterns, and consistency requirements. This shows you understand that system design isn't about memorizing architectures but about making informed decisions based on specific needs.
You're evaluated on four main things: system architecture design and technical depth carry the most weight, since they directly test your ability to build scalable systems and show deep understanding of the technologies involved. Trade-off reasoning and communication clarity round out the evaluation, focusing on your ability to justify decisions and explain complex concepts clearly.
The interactive nature means you'll be refining your design based on the interviewer's questions and new requirements. They might introduce new constraints midway through or ask you to optimize for a different metric, testing your ability to adapt your architecture thoughtfully rather than just defend your first approach.
Don't just name-drop technologies without being able to explain their strengths and limitations. If you mention using Redis for caching, be prepared to discuss when it might not be the right choice and what alternatives you'd consider.
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Most commonly asked System Design questions
OpenAI
Staff
Onsite
Coding
The format is effectively the same as your earlier technical coding screen, just with different problems.
You can choose to work in your own IDE with screen sharing, which most staff candidates prefer since it lets you use your familiar environment and shortcuts. The alternative is using CoderPad, but given how advanced these problems are, having access to your normal development setup often makes the difference between a smooth experience and struggling with an unfamiliar interface.
Choose a programming language you know extremely well for this round. You'll want to take advantage of language-specific features and standard library functions to implement clean, efficient solutions quickly.
You're evaluated on four main things: problem-solving and algorithms carry the highest weight, since they test your ability to tackle complex programming challenges with optimal data structures and efficient approaches. Code quality and scalability matter equally, reflecting the staff-level expectation that your solutions should be production-ready and demonstrate architectural thinking about how they'd integrate into larger systems. Testing and thoroughness round out the evaluation, focusing on your attention to edge cases and ability to reason through correctness, while communication tests how well you explain your approach and guide the technical conversation.
The interviewer will definitely introduce additional constraints or ask for optimizations after you complete the initial solution, often testing your ability to think about scalability and production concerns. This iterative aspect mirrors the staff-level responsibility of evolving technical solutions as requirements change, and they want to see how you balance immediate functionality with long-term architectural thinking.
Avoid the temptation to over-engineer your solution from the start. Focus on getting a working implementation first, then optimize based on the interviewer's follow-up questions rather than trying to anticipate every possible enhancement.
Live, up-to-date
Most commonly asked Coding questions
OpenAI
Staff
Architecture / System Design
The system design round during your final interview loop is the most technically challenging conversation you'll have throughout the entire process, and it carries enormous weight in determining whether you get an offer at the staff level. Again, the format mirrors your earlier system design screen, but you'll get a different problem and face deeper technical questioning.
The interactive nature means you're constantly adapting your design based on the interviewer's probing questions and evolving requirements. They'll start by asking you to sketch the high-level architecture, then drill down into specific components when they want to test your technical depth. Expect questions like "How would you handle a 10x traffic spike during a product launch?" or "What happens if your primary data center goes offline for six hours?" The interviewer wants to see you think through failure scenarios and show that you understand the operational complexities of running distributed systems in production.
"In your system design interview, plan on taking an example and going through your entire design. Learning to drive system design interviews is key."
— Recent OpenAI Staff candidate
Start by spending 5-10 minutes clarifying the exact requirements and constraints before drawing anything on the whiteboard. Ask about expected scale, read/write patterns, consistency requirements, and uptime expectations. This shows you understand that good system design starts with understanding the problem thoroughly.
You're evaluated on four main things: scalable system architecture and technical breadth carry the highest weight, since they directly test your ability to design systems that can handle OpenAI's scale while showing deep understanding of the underlying technologies. Trade-off analysis and interactive problem-solving round out the evaluation, focusing on your ability to make informed decisions about competing approaches and collaborate effectively during the design process.
The key to success here is balancing high-level architectural thinking with the ability to dive deep into implementation details when pressed. You need to show you can reason about load balancers, database sharding strategies, caching layers, and message queues, but also step back and explain how all these pieces work together to solve the business problem. The interviewer will probably introduce new constraints or requirements midway through, testing your ability to evolve your design gracefully rather than starting from scratch.
Don't just name-drop technologies without being able to explain their trade-offs. If you suggest using Kafka for event streaming, be ready to discuss when you might choose Pulsar instead, or explain how you'd handle message ordering guarantees.
Live, up-to-date
Most commonly asked System Design questions
OpenAI
Staff
Behavioral — Leadership
This is typically a 45-minute conversation with a senior manager or executive.
This interview focuses specifically on your leadership capabilities and ability to drive technical strategy at an organizational level. The interviewer is evaluating whether you can influence decisions across teams, mentor and develop other engineers, and align technical work with OpenAI's strategic objectives. They're looking for evidence of your impact on technical direction, team productivity, and organizational capability building.
You'll get scenarios that explore your experience leading technical initiatives, making architectural decisions that affected multiple teams, and mentoring engineers at different levels. They want to understand how you've driven consensus around complex technical decisions, influenced engineering culture, and balanced competing priorities when resources are constrained. Expect deep dives into your decision-making process and the long-term impact of your leadership choices.
Prepare 3-4 detailed stories using the STAR method that showcase different aspects of technical leadership: driving architectural decisions, mentoring and developing other engineers, influencing cross-team technical strategy, and leading through significant technical challenges or organizational changes.
You're evaluated on four main things: technical leadership and influence carry the highest weight, since they need evidence you can drive technical decisions at scale and guide teams through complex challenges. Decision-making under uncertainty and organizational impact round out the evaluation, focusing on your ability to make sound technical judgments with incomplete information and your track record of building technical capability within organizations.
The most successful candidates come prepared with concrete examples of times they've set technical direction for multiple teams, mentored engineers who went on to senior roles, or led major technical initiatives that delivered significant organizational impact. OpenAI needs staff engineers who can operate effectively at the intersection of cutting-edge research and production systems.
Avoid stories that focus primarily on individual technical contributions. Staff-level evaluation requires evidence of leadership impact: times you influenced technical strategy, developed other engineers, or drove organizational technical decisions.
Behavioral — Collaboration
This is typically a 30-minute conversation with a team member.
This interview focuses on your ability to collaborate effectively across disciplines and maintain productivity in OpenAI's fast-moving, research-driven environment. The interviewer is evaluating how you work with diverse teams including researchers, product managers, and safety teams, and whether you can stay effective when priorities shift rapidly based on research discoveries or product requirements.
You'll get scenarios about cross-functional collaboration, resolving technical disagreements with non-engineering stakeholders, and adapting when project requirements change mid-stream. They want to understand how you navigate the friction between research timelines and product delivery, communicate technical concepts to diverse audiences, and maintain team effectiveness when facing ambiguity about technical feasibility or product direction.
Prepare 2-3 detailed stories that showcase your ability to collaborate across disciplines, resolve conflicts constructively, and maintain productivity when facing ambiguous or changing requirements. Focus on examples from research-driven or rapidly evolving technical environments.
You're evaluated on three main things: cross-functional collaboration and communication clarity carry the highest weight, since staff engineers regularly work with researchers and executives who have different technical backgrounds and priorities. Adaptability under uncertainty rounds out the evaluation, focusing on your ability to stay productive when technical requirements or project scope changes rapidly.
Avoid generic teamwork stories. Focus on examples that demonstrate your ability to work effectively in research-driven environments where technical requirements may be unclear and priorities can shift based on experimental results.
Technical Project Presentation
The technical project presentation is your chance to show off the depth of your engineering experience through a detailed retrospective of your most impactful work. It's a 45-minute interview where you will present on a past project.
Unlike the live coding or system design rounds where you're solving new problems on the spot, this presentation lets you shine by diving deep into a project you know inside and out. You'll join a video call and share your screen to walk through slides covering the problem you solved, your technical approach, the challenges you hit, and the ultimate impact of your work. The interviewer will ask clarifying questions throughout, probing into your decision-making process and technical reasoning.
Picking the right project is crucial for success here. Choose something where you drove the technical strategy and influenced organizational decisions, ideally a multi-quarter initiative that involved significant technical challenges and cross-team coordination. The best presentations tell a compelling story that shows multiple aspects of staff-level engineering: setting technical direction, building organizational capability, handling uncertainty, influencing stakeholder decisions, and measuring success through both technical and business metrics.
Structure your presentation around 30 minutes of content to leave ample time for questions. The Q&A portion is where you can really demonstrate your technical depth, so don't rush through your slides just to cover everything.
You're evaluated on four main things: technical leadership and strategic impact carry the highest weight, since they directly test your ability to drive technical decisions at an organizational level and deliver results that matter to the business. Communication clarity and decision-making insight round out the evaluation, focusing on your ability to influence stakeholders and navigate complex trade-offs while building consensus around technical direction.
The interactive nature means you should prepare for deep technical questions about your architecture choices, alternative approaches you considered, and how you might handle different requirements or scale constraints. The interviewer wants to understand not just what you built, but why you built it that way and what you learned from it.
Avoid getting caught up in implementation details that don't showcase staff-level thinking. Focus on the strategic technical decisions, organizational impact, and leadership challenges rather than walking through code or day-to-day execution details.
(Optional) Domain-Specific Interview
For certain staff roles, particularly those focused on ML infrastructure, distributed systems, or AI safety, you may have an additional 60-minute domain-specific technical interview. This round dives deep into the specific technical domain you'd be working in, testing both your theoretical understanding and practical experience with relevant technologies and challenges.
For ML infrastructure roles, expect questions about model deployment pipelines, distributed training orchestration, GPU resource management, and serving optimization. For distributed systems roles, you might discuss consensus algorithms, data consistency models, fault tolerance patterns, and large-scale data processing architectures. AI safety-focused roles could explore alignment techniques, interpretability methods, or robustness evaluation frameworks.
If you're told this interview is part of your loop, spend significant time reviewing the specific technical domain. Staff-level domain interviews expect you to discuss cutting-edge techniques, industry best practices, and trade-offs between competing approaches at a very deep level.
The evaluation focuses on technical expertise depth, practical application experience, and your ability to reason about complex domain-specific trade-offs. They want to see that you can contribute immediately to highly specialized technical challenges while also understanding how your domain expertise connects to broader organizational objectives.
Don't treat this as a general technical interview. The questions will assume deep domain knowledge and expect you to discuss nuanced technical decisions, performance characteristics, and implementation challenges specific to your area of expertise.
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