OpenAI Interview Questions — Page 53

OpenAI·Machine Learning Engineer·Technical Phone Screen

Jun 2026

Interviewed for an ML engineer role at OpenAI and got a question about AI deployment risks that I thought I was ready for but kind of fumbled the structure on.

What risks can emerge when deploying AI systems in production, and what strategies exist to mitigate them?

“I went straight to model drift and data poisoning, which felt solid, but then I kind of stalled when they pushed on societal-level risks.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

Interviewed for a data science role at OpenAI. One question that stuck with me was about working with messy or historical data when you can't just run a clean experiment.

When you can't run a controlled experiment, how do you make decisions using imperfect or historical data?

“I fumbled the opener a bit, started listing techniques before actually grounding it in a real scenario.”

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OpenAI·Software Engineer·Onsite - Cross-functional / Panel

Jun 2026

Did a cross-functional round at OpenAI for a software engineer role, which basically meant talking to a non-engineer about how I work with people outside my team. Felt more like a behavioral chat than anything technical, which honestly threw me a little since I wasn't expecting it.

Can you walk me through a time you worked closely with non-engineering partners and how you navigated that collaboration?

“Wasn't expecting this round at all so I kind of fumbled the opener.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

System design round at OpenAI for a SWE role, centered entirely on a location-based service problem similar to Yelp or Google Maps. The depth they expected on geospatial indexing was pretty serious, this wasn't a 'draw some boxes and call it a day' kind of interview.

Design a scalable system to help users discover nearby points of interest like restaurants and shops. The system needs to handle 500 million locations globally, 100k queries per second, and return results in under 100 milliseconds.
How would you design a system to find exactly the K nearest locations to a user? Walk through your indexing approach and explain how you handle edge cases near cell boundaries.
How would you build and shard a geospatial index across multiple servers? What happens when certain geographic areas generate disproportionately high traffic?
Compare Geohash and QuadTree for a POI lookup feature. What are the practical tradeoffs between them?
Walk me through the internals of Geohash: how does the encoding work, what's the cell size at different precision levels, and are there any edge cases geographically?
How would you handle updates to business data in a Yelp-like system, such as a restaurant changing its hours? How do you keep cached results fresh?
How would you design a QuadTree to find the exact N nearest points, and what optimizations would you apply?

“This is the core question and it's deceptively broad.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

Got a system design coding hybrid at OpenAI for a software engineer role. The problem was about managing a GPU credit pool across tenants, which sounds straightforward until you actually have to think through all the edge cases around concurrency and crash recovery.

You're managing a GPU pool with per-tenant credit balances. Given a stream of signed-integer events (positive = add credit, negative = deduct), implement three functions: init(events) to set up state from an ordered list of events, getBalance(tenantId) to return a tenant's current balance, and applyEvent(event) to process a new event and update state. You also need to define the event schema, decide whether to reject events that would push balances negative, handle idempotency for repeated event IDs, and discuss data structures, concurrency, and crash recovery. Target O(1) average per operation after O(n) init.

“I went straight to a hashmap keyed by tenantId for O(1) reads and updates, which they seemed fine with.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

OpenAI Research Engineer interview that went deep into dependency resolution algorithms. One meaty system design problem that sprawled into a bunch of sub-topics I wasn't fully ready for.

Design a package dependency version resolver: given a set of packages with multiple available versions and a set of dependency constraints, find a valid assignment of one version per package that satisfies all transitive constraints, or determine that no valid resolution exists.

“I started with a naive BFS approach and the interviewer let me run with it for a bit before asking what happens in a diamond dependency case.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

Did a technical round at OpenAI for a software engineer role. The problem sounded simple until it wasn't, basically a key-value store with a serialization twist baked in.

Implement a key-value store with put, get, and delete operations, where values are structured objects that must be serialized to strings for storage. You need to define the serialization format and ensure values survive a round-trip correctly.

“I went straight for the basic operations and got those working fine, but then the serialization part tripped me up more than I expected.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026

Went through a system design round at OpenAI for a software engineer role. The problem was a memory allocator, which sounds like a classic OS topic until they want you to actually spec out the data structures and hit O(m log m) total runtime. Pretty intense.

Design a memory allocator that supports allocate(size) and free(addr) operations over a contiguous region of size n, handling up to m operations. allocate should find the leftmost free block of sufficient size and split it if needed. free should mark the block free and coalesce with adjacent free neighbors. Target O(m log m) total runtime independent of n.

“This one took me a while to get traction on.”

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OpenAI·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026

Behavioral round with an engineering manager at OpenAI, about 45 minutes. Pretty standard BQ territory but a few questions had follow-ups that pushed into specifics, which I wasn't fully ready for.

What are you looking for in your next role?
Tell me about a time you worked on something that seemed impossible.
How do you handle conflict with a teammate or colleague?
What's a piece of critical feedback you've received?
Walk me through a project that didn't go as planned.
Tell me about a time you failed.

“Felt like a warmup but I rambled a bit.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026

OpenAI SWE interview that leaned hard into NumPy fundamentals. Had to implement 1-Nearest-Neighbor from scratch and then build a small feedforward neural network, both without any ML libraries. The follow-up asking me to swap L2 for L1 distance was where things got interesting.

Implement a 1-Nearest-Neighbor classifier in NumPy using L2 distance. Given training data and labels, predict the label of each test sample by finding its closest training point.
Build a simple feedforward neural network (MLP) from scratch using only NumPy, including at least the forward pass with linear layers and non-linear activations.
Follow-up: change the 1NN implementation to use L1 (Manhattan) distance instead of L2. How do you compute pairwise L1 distances efficiently in NumPy?

“The vectorized pairwise distance part is where most people trip up and I was no exception.”

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