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OpenAI interview notes, page 49 of 67

Mid-sized

Most of these OpenAI write-ups come from Software Engineers (460), then Machine Learning Engineers (137) and Backend Engineers (23). The most reported rounds are the technical phone screen (335), the system design round (229), the onsite coding round (32) and the behavioral / leadership round (22).

667 interview notes · updated Jul 2026

OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a software engineer role, centered on a single meaty problem about credit accounting for GPU jobs. The question had a lot of moving parts and I think I handled the basics okay but stumbled when they pushed on concurrency and scheduling extensions.
  • Design a GPU credit accounting system where users have balances, jobs consume credits based on GPU type and duration, and the system supports granting/refunding credits, reserving credits at job start, deducting on completion, rejecting jobs with insufficient funds, and reporting per-user usage.

“I started with the data model, which felt natural: a users table with a balance column, a reservations table keyed on job ID, and a usage log for reporting.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Got a technical screen for an MLE role at OpenAI that went pretty deep into type systems and compiler theory, which I wasn't fully expecting. The problem was meaty and the follow-up discussion pushed into territory I hadn't prepped for.
  • Given the AST of a small toy language where nodes can represent literals, variables, function calls, or composite expressions, implement a recursive function that infers the type of each expression. Your solution should handle nested expressions, type propagation through function return values, and raise clear errors on type mismatches. Also discuss how you'd extend the system to support generics or union types, and analyze time complexity relative to AST size.

“This one took me a minute to even parse.”

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OpenAI·Backend Engineer·Technical Phone Screen

Jun 2026
OpenAI backend interview that threw a full interpreter design problem at me. Not your typical coding round, this one wanted tokenization, parsing strategy, expression evaluation, the works. Left feeling like I'd only scratched the surface of what they were actually looking for.
  • Build a toy language interpreter from scratch: support variable assignment, arithmetic, conditionals, and output statements. Walk through tokenization, your parsing approach, how you evaluate expressions, and how you handle edge cases like undefined variables and operator precedence.

“This wrecked me a little.”

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OpenAI·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
OpenAI SWE coding round with one meaty interval-management problem that looks like a merge-intervals warm-up but has enough edge cases to chew through your whole session if you're not careful.
  • Design a shard management system where each shard has an id, start key, and end key. Implement add_shard, remove_shard, and a rebalance() method that ensures no more than `limit` shards overlap at any key point. When overlap would exceed the limit, trim the newer shard's range. Fill any gaps that open up after trimming to keep coverage contiguous.
  • How does your rebalance logic handle the case where multiple shards all start at the same key and the limit is small? Walk through what happens step by step.

“I went straight to merge-intervals instinct and that got me most of the way there, but the gap-fill requirement blindsided me.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a software engineer role, focused entirely on building a ChatGPT-style conversational AI service. The scope was massive and the interviewer kept pushing into GPU infrastructure and serving stack details I wasn't fully prepared for.
  • Design a ChatGPT-style conversational AI service from scratch. Walk through functional requirements, architecture, and key trade-offs.
  • How would you handle streaming token responses to users with low first-token latency as a hard requirement?
  • How would you manage conversation history and long context windows, especially when the conversation exceeds the model's context limit?
  • Walk me through how you'd design the GPU inference serving stack, including autoscaling under variable load.
  • How would you implement rate limiting and quota enforcement across a high-concurrency AI API?
  • How would you design a safety and moderation pipeline for user inputs and model outputs?
  • What observability would you build into this system, and how would you debug a sudden spike in latency?

“I started with the obvious stuff like multi-turn chat and user accounts, which felt fine.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a SWE role. One question, URL shortener, which sounds straightforward until you actually have to talk through key generation, caching, and global read scaling in a coherent way.
  • Design a URL shortening service similar to Bit.ly or TinyURL, covering key generation, redirect handling, storage, caching, and scaling.

“I started with the redirect path because that felt most concrete, then worked backwards to key generation.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Research Engineer interview at OpenAI that was basically a build-a-mini-interpreter problem. More open-ended than I expected, with a lot of follow-up discussion about language design tradeoffs rather than just grinding out code.
  • Implement an interpreter for a small toy language that supports commands like SET, ADD, and PRINT operating on a key-value variable store. Given a sequence of commands, execute them and produce the correct output.
  • How would you extend this interpreter to support variable scoping, conditionals like IF, loops like WHILE, and function calls with CALL and RETURN?

“I went straight for the direct-execution approach, just parsing each line and dispatching on the command name.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Did a coding round for an ML Engineer role at OpenAI and got a multi-part grid infection problem. Five parts total, each one layering on more rules. It was a lot to get through in one session.
  • Simulate how an infection spreads across a 2D grid of plants, starting from initially infected cells and spreading to healthy neighbors each time step. Solve this across five progressively harder variants that add rules like immunity, variable spread rates, multiple infection sources, and grid mutations. For each part, return either the total time to full infection or the grid state after k steps.

“Basically a rotting oranges problem but they kept piling on rules.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a software engineer role. The problem was open-ended enough that I spent half the time figuring out what to even focus on, and never really got into a groove.
  • Design a cloud-based IDE system.

“Drew a rough high-level diagram first, then tried to go deep on worker node lifecycle management.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Did a technical screen for a Software Engineer role at OpenAI. The whole thing was one big OOD problem around simulating a turn-based monster battle. More design-heavy than I expected for a phone screen.
  • Design an object-oriented model and implement the core battle logic for a turn-based fight between two teams of monsters. The system should simulate the battle round by round and produce a detailed chronological battle log plus a final result.

“This was the entire interview.”

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