OpenAI Interview | Software Engineer (Applied AI) | November 2025

Interview rounds

The interview process consisted of:

1. Recruiter Screen - Initial outreach via third-party contractor (c-openai.com email), then handed off to actual OpenAI recruiter
2. Technical Phone Screen - Two separate 60-minute rounds:
   • System Design
   • Coding
3. Virtual On-site - 4-5 rounds total:
   • Coding Round
   • System Design Round
   • Standard Behavioral Round
   • Cross-functional Behavioral Round
   • Technical Deep Dive (past project presentation)

Interview questions by round

Recruiter screen (Third-party contractor, then OpenAI recruiter)

Third-party contractor questions:

• Talk about a project that you did
• Why are you interested in OpenAI?
• Basic background questions

Compensation: No discussion about money at any point in the process

Level: Asked candidate's current level. OpenAI interviews senior and staff candidates the same way, then decides level after interviews

Technical phone screen - Round 1: System design (60 minutes)

Main Question: Design Slack

Candidate's reaction: Surprised it was such a "very famous" and standard system design question - expected something more OpenAI-specific

Focus areas:

• Getting a regular solution working first
• Main deep dive: Scalability
  • "What if we 100x'd the scale? 1000x'd the scale?"
  • "How would this particular design scale with that?"
  • Agreed the original design would work for agreed-upon scale, but pushed deeper on scaling assumptions
  • "How would these assumptions stand the pressure test?"

Additional context mentioned:

• Asked about monitoring token usage: "If we have a system giving out tokens and companies are using them in different amounts, how do we monitor all the token usage?"

Interviewer engagement: Very engaging

Note: Possibly related to OpenAI building a Slack-like product (mentioned by interviewer who talked to a PM recently)

Technical phone screen - Round 2: Coding (60 minutes)

Main Question: Given a map (key-value pairs), efficiently store that state and retrieve that state to/from the cloud

Setup:

• Provided helper methods that convert string values to binary and binary back to string
• Write functions to do both operations efficiently
• Don't take up too much space, be relatively efficient

Characteristics:

• Not very algorithmic
• More about optimizations and interacting with "third-party API" (actually just a hardcoded method in the coder pad)
• Method was like "string to byte" with logic to handle saving/retrieving data to/from cloud
• Similar to reaching out to another service - more indicative of day-to-day work

Interview approach:

• Focus on completeness first, then optimizations
• "Let's get something that works, then we can talk through optimizations"
• Later parts of interview were about optimizations themselves
• Didn't want candidate getting hung up on optimizing too hard initially

Test cases:

• They kept picking at: "How would we handle this? How would we optimize this particular state?"
• Looking for testing mindset - testing through all edge cases before starting to code
• "Here are the test cases - what are the possible edge cases you can hit?"
• More emphasis on edge cases at beginning than typical interviews

Practical rating: 3-4 out of 5 (not purely theoretical, but not completely practical like Cursor's "here's our codebase" approach)

On-site round 1: Coding

Main Question: Excel cell problem - implement cell formulas and dependencies

Setup:

• "Here are all these cells. Here are some helper functions you can reach out to."
• Write formula/function so you can do Excel-style operations:
  • "= cell2 + cell3" notation
  • How does formula update when dependent cells change?
  • Cell dependencies and cascading updates

Focus areas:

• Small optimizations needed
• Caching: When do you want to cache? Don't want to cache every single value because it's slow
• Not pure LeetCode/algorithmic, but very practical
• Recreating something that exists but you don't think about often

Candidate's reaction:

• Thrown off guard - expected LeetCode medium/hard problems based on recruiter guidance
• Seemed more like systems design than coding, but "hand in hand"
• Didn't have to flex "crazy algorithm" skills
• Flexed problem-solving more than algorithms
• Required good understanding of basic algorithms, caching, cache invalidation

Similar to phone screen coding: Similar subject matter/approach to the earlier coding round

On-site round 2: System design

Main Question: Design a streaming platform at scale

Theme observed: Really focusing on scale (consistent across both system design rounds)

Iterative process:

• Start with standard solution for smaller/set of users
• As interview progresses, deeper and deeper questions:
  • "How do we handle different frame rates?"
  • "How do we handle different people across the world?"
  • "How do we handle 10x, 100x, 1000x scale to the user base?"

Candidate's surprise: Expected OpenAI-specific questions (tokenization, etc.) since other companies try to "bake that into their interview process," but this was a standard streaming platform question

Note: Interviewers at OpenAI have no question banks - they ask whatever they want (no pre-approval process). Candidate suspects many interviewers port questions from previous companies.

On-site round 3: Standard behavioral

Main themes:

1. Adaptability/Reacting when things go awry:
   • "Tell me a challenging situation when..."
   • How do you adapt when things pop up?
   • When situations go awry, how do you react and what are the next steps forward?
   • Different from typical "tell me about your most impactful project"
2. Culture fit:
   • "Why are you picking OpenAI?"
   • "What are your motivations?"
3. How you work:
   • "What is a standard day as a software engineer? What does that look like?"
   • Looking for: People who aren't heads-down coding 24/7
   • Want people who approach problems in different ways
   • Want generalists: People who can write docs, solve problems, AND be really good at technical coding

Conflict: Mentioned but seems to be more covered in cross-functional round

On-site round 4: Cross-functional behavioral

Focus: Cross-functional collaboration and conflict

Example questions:

• Tell me about a time you worked with a legal team or another team
• How would you handle this scenario? (cross-functional scenarios)

Note: Candidate thought this was interesting - had never experienced this type of specialized behavioral round before

On-site round 5: Technical deep dive (under 60 minutes)

Format: Talk about one particular project in depth

Approach:

• Start with outline: "Tell us about one of your most exciting projects"
• Then dive deeper into specific areas

Areas of deep dive:

1. What you actually did (cutting through the BS):
   • "What actually did you do?"
   • "What did you lead? What did you actually change?"
   • Not satisfied with flashy numbers and 2-minute STAR answers
   • Really asked "what and why" - "Why did you do this project in the first place?"
2. Technical aspects:
   • "What were some of the technical challenges?"
3. Non-technical/stakeholder challenges:
   • "What were some of the non-technical, stakeholder-esque challenges?"
4. Working relationships:
   • "Who were you working with?"
   • "What was that working relationship like?"
   • "How did you work with them?"
5. Learnings:
   • Deep focus on actual learnings from the project

Comparison to typical behavioral:

• OpenAI: Really talking deeply about learnings, what you actually did, and how you actually did it
• Can't give 2-minute STAR answers - need to be prepared for extensive probing
• Interviewers ask follow-up after follow-up to cut through BS

Candidate's reaction:

• Most unique part of the interview
• "Nothing I've ever experienced before"
• Kept them on their toes
• Felt need to really know what you're talking about when discussing a project
• Good way of verifying what the person actually did vs. BS

Possible training: Candidate suspects OpenAI (and Anthropic) have interview training on how to investigate projects, as this approach is "starkly different" from normal big tech interviews. Initial question starts it, then "all the action happens in the probing."

Key themes & insights

Interviewer engagement: 3.5-4 out of 5 overall (pretty solid, no one below 3)

Accuracy of prep: 3-4 out of 5 (mostly accurate except coding questions weren't shaped like expected LeetCode medium/hard)

Theoretical vs Practical: 3-4 out of 5 (more practical than pure algorithms, but not as practical as Cursor's "here's our codebase" approach)

Applied AI team: Seems to be most of the company (80%+) outside of core ML research/modeling.

Outreach method: Third-party contractors (c-openai.com emails) do initial outreach, then hand off to actual OpenAI recruiters who shepherd through process

No compensation discussion: Never discussed at any point

Practical focus: Questions recreate things that exist (Excel, Slack, streaming platforms) rather than pure algorithms

Scale obsession: Consistent theme across all technical rounds - always pushing on "what if we 10x/100x/1000x the scale?"

Problem-solving focus: Looking for people who approach problems from first principles, are generalists, and can dive deep into how they work

No question banks: Interviewers ask whatever they want with no pre-approval process, leading to unpredictable questions

TL;DR from candidate: "Pretty practical questions. Not asking cutting-edge algorithmic problems. Looking for problem solvers who approach from first principles and can dive deep into their experiences and how they work."