OpenAI Interview Questions — Page 2

OpenAI·Software Engineer·Technical Phone Screen

Apr 2026

Got a coding round for a Software Engineer role at OpenAI that was pretty deep into type system territory. The core problem was implementing type inference for a toy language, which sounds manageable until you're actually in it and realize how many edge cases there are.

You have a Node class representing types in a small toy language. Nodes can be primitives (char, int, float), generic placeholders (T1, T2, etc.), or tuples of other Nodes. Implement an infer_return(input_type, actual_type) function that walks both type trees simultaneously, records what concrete types each generic maps to, raises an error on conflicting bindings or structural mismatches, and returns the final generic-to-type mapping. Then discuss how you'd extend this to nested generics and use the result to resolve a return type.

“I started okay, got the basic tree walk going and handled the primitive case fine.”

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OpenAI·Machine Learning Engineer·Onsite - Behavioral / Leadership

Apr 2026

Behavioral round at OpenAI for an ML engineer role. One question, pretty focused on how you navigate technical conflict with cross-functional partners.

Describe a situation where you faced a technical challenge that also involved a disagreement with a partner team. How did you resolve it?

“I had a story ready but partway through I realized I was spending too long on the technical side and kind of glossing over the actual conflict resolution part, which is probably what they cared about more.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026

Coding round at OpenAI for a software engineering role. The problem was an IP address iterator with CIDR support, which sounds manageable until you're actually in it and realize there are a dozen edge cases waiting to bite you.

Extend an IPv4 iterator class to support next() and prev() traversal over the full 32-bit address space, returning addresses as strings with wrap-around at both ends.
Extend the same iterator to accept a CIDR block at construction (like '192.168.0.0/24') and constrain both forward and backward traversal strictly within that address range.

“The base version felt fine.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026

OpenAI Research Engineer interview with a meaty systems/languages question. The whole thing was basically one extended coding problem that kept growing, which I wasn't fully ready for.

Build an interpreter for a small toy programming language supporting variable assignment, basic arithmetic, and print statements. Walk through tokenizing input, parsing into statements or an AST, evaluating with a variable environment, and handling errors like undefined variables or type mismatches.

“I spent too long on the tokenizer and ran out of steam by the time we got to error handling.”

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OpenAI·Software Engineer·Hiring Manager Screen

Apr 2026

Behavioral round with the hiring manager at OpenAI for a software engineer role. Heavy on values and mission alignment, plus the usual failure and conflict questions. Pretty intense if you haven't thought hard about your actual views on AGI before walking in.

Why OpenAI specifically, and what unique value do you bring that fits the mission and culture here?
What's your personal take on AGI, including timelines, risks, alignment, and how a frontier lab should balance capability development against safety concerns?
Tell me about a project that failed and what you took away from it.
Describe a significant mistake you made and how you handled it.
Tell me about a time you received critical feedback and how you responded.
Walk me through a conflict you had with a manager or peer and how it was resolved.

“I had a decent answer prepared but it felt rehearsed and I knew it.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026

OpenAI system design round for a software engineer role. The whole thing was one big distributed systems problem that required me to think about message propagation, deduplication, and topology inference simultaneously. Pretty intense for a single question.

Given a distributed cluster of machines connected in some topology, implement a receiveMessage(msg, fromMachine) function on each node so that messages propagate correctly through the network. Each node should be able to infer the total machine count and key topology properties (neighbors, reachability) purely from the messages it observes. Focus on the receive-handling logic, message format, idempotency, deduplication of repeated messages, and how topology information accumulates over time.

“I started with flooding and immediately got asked how I'd prevent cycles.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026

System design round at OpenAI where they had me build out a calendar app from scratch. The follow-up about multi-device sync is where things got interesting and honestly a bit uncomfortable.

Design a calendar application similar to Apple Calendar, supporting day, month, and year views where users can create and manage events.
How would you extend the calendar to support multi-device sync, so an event created on one device automatically appears on a user's other devices?

“I jumped straight into the UI layer because the prompt mentioned views explicitly, which I think was a mistake.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Apr 2026

OpenAI ML Engineer technical screen, basically a deep dive into building a neural network from scratch using only NumPy. No frameworks, no autograd, just raw math and code. Felt like a grad school exam more than a typical ML interview.

Implement a small feed-forward neural network from scratch using only NumPy, including the forward pass, loss computation, backpropagation, and a gradient descent update step.
How do you handle numerical stability when computing softmax, and what is the log-sum-exp trick?
Walk through how shape and broadcasting considerations affect your implementation when extending from a single sample to mini-batch training.

“This was the whole interview, basically.”

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OpenAI·Machine Learning Engineer·Onsite - System Design / Architecture

Apr 2026

OpenAI MLE interview with a system design question focused on building an in-memory GPU credit tracking system. Pretty deep technically, they wanted actual implementation decisions not just handwaving about data structures.

Design and implement an in-memory GPU credit tracking system with operations to add credits (with expiry timestamps), consume credits (expiring stale ones first and consuming oldest-expiring credits first), and check a user's current balance. Discuss your data structure choice and the time complexity of each operation.

“I went with a min-heap keyed on expiry timestamp pretty quickly, which felt right, but then they pushed on the trade-offs versus a sorted list or a sorted container from something like sortedcontainers.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026

System design round at OpenAI for a backend/infrastructure role. The question was a full payment platform design, which sounds scoped until you realize they want fraud, reconciliation, idempotency, AND compliance all in one go. Left feeling like I covered maybe 60% of what they were looking for.

Design a payment processing system that supports cards, bank transfers, and digital wallets, with idempotent transactions, retry logic, refund flows, fraud detection, and reconciliation against external payment processors. Address consistency, scalability, failure handling, and audit/compliance requirements.

“I spent the first chunk of time on the data model and API layer, which felt safe but probably wasn't what they cared most about.”

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