OpenAI Interview Questions — Page 34

OpenAI·Product Manager·Onsite - Product Sense / Strategy

May 2026

PM interview at OpenAI, one question about defining success metrics for a new collaborative workspace feature inside ChatGPT. Pretty focused session, product strategy and metrics heavy.

OpenAI is planning to launch a collaborative workspace for teams within ChatGPT. How would you define success metrics for it?

“I went straight for engagement metrics and kind of forgot to anchor on what 'collaborative' actually means in this context.”

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OpenAI·Software Engineer·Onsite - Coding / Algorithms

May 2026

Coding round at OpenAI for a software engineer role, focused entirely on a social graph problem that kept building in complexity across four parts. Got through most of it but ran out of steam on the last one.

Design a FriendCircle class that supports follow, unfollow, and snapshot operations.
Given a snapshot of the social graph, return all friends of a given user.
Given a snapshot, implement a friend recommendation feature.
Compare two snapshots of the social graph and identify what changed in a user's friend list between them.

“Seemed straightforward at first.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026

System design round at OpenAI for a software engineer role, 60 minutes on designing a cloud-based IDE. The interviewer was pretty relaxed and framed their role as guiding rather than grilling, which made the whole thing feel less like an interrogation.

Design a cloud-based IDE from scratch.

“Went in without having specifically prepped for system design, which was a mistake in hindsight.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

May 2026

Got a pretty gnarly coding question for an ML Engineer role at OpenAI, basically a heavily extended version of the classic rotting oranges grid problem with a bunch of new mechanics layered on top. Felt like they wanted to see if you could handle simulation complexity without losing your head.

Given a grid where cells can be infected, healthy, or immune, simulate a multi-rule infection spread over time. Two additional parameters control when a plant becomes 'severely infected' (based on how many infected neighbors it has) and how many days after that it dies. Return the number of days until nothing changes and the total number of dead plants.

“This is LC 994 but they kept adding rules until it barely resembled the original.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

OpenAI SWE interview with a graph-based recommendation problem. The core of it was designing a 'people you may know' feature on top of a follow graph, which sounds approachable until you get into the edge cases and complexity analysis.

Given a social graph that supports follow and unfollow operations, implement a function to recommend the top K users for a given user U to follow, ranked by how many of U's existing followees also follow each candidate. Discuss edge cases and the complexity of your approach.

“I got the basic loop right pretty quickly: iterate over U's followees, then over each of their followers, accumulate counts in a hashmap, then use a min-heap to pull out top K.”

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OpenAI·Technical Program Manager·Hiring Manager Screen

May 2026

Interviewed for a TPM role at OpenAI. Only one question to speak of, so hard to say much about the overall process.

How do you collaborate with engineers?

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OpenAI·Software Engineer·Onsite - Multi Round

May 2026

OpenAI SWE loop includes a technical deep dive where you present slides on a past project and get grilled hard. The bar is genuinely high and reaching the later rounds (HM, behavioral) only happens if you clear the technical ones first.

Walk us through a past project in depth, covering the technical decisions you made and why.
Why did you choose this approach over alternative X for your system?
How would your system hold up if the load increased by 10x?

“The format is slides, and the interviewer will dig into whatever you put up there.”

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OpenAI·Data Scientist·Technical Phone Screen

May 2026

Got a case-style question at what seemed like a technical screen for a DS role at OpenAI. The whole thing was one big experiment design problem about a subscription promotion, and it went pretty deep into causal inference, ROI estimation, and stakeholder communication. Felt like a take-home dressed up as a live interview.

An online subscription product wants to test giving eligible new users their first month free instead of charging upfront. Design a full experiment and analysis plan to estimate the causal impact of this promotion.
What should the primary decision metric be for this experiment, and why are simpler metrics like signup rate or retention among signups not enough on their own?
How would you measure both short-term and long-term impact when the free month shifts the timing of revenue and changes who signs up?
How would you estimate ROI when the first month is free and some downstream revenue data is missing or only partially observed?
What are the key pitfalls in this kind of experiment, covering things like selection bias, delayed conversion, seasonality, cannibalization, and heterogeneous treatment effects?
If your dataset is limited or imperfect, what assumptions or sensitivity analyses would you run?
How would you present your recommendation from this experiment to a non-technical stakeholder?

“This is a monster of a question.”

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OpenAI·Software Engineer·Technical Phone Screen

May 2026

Interviewed for a software engineering role at OpenAI and got a type-system design question that felt more like a PL theory exercise than a typical coding screen. Interesting problem but I was not fully prepared for the data structure angle.

Design a Node class for a small toy language's type system that supports primitives (char, int, float), generic placeholders (T1, T2, ...), and tuples of Nodes. Implement constructors for each variant and a toString function that produces canonical string output like '(int, T1, (float, T2))'. The design should also make type inference easier down the line.

“I spent the first few minutes just trying to figure out whether to use a tagged union or subclassing.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

May 2026

System design round at OpenAI for a software engineer role, focused entirely on building a text-to-video generation service like Sora. Heavy emphasis on GPU constraints and async job infrastructure. The depth they expected was pretty intense for a single session.

Design a text-to-video generation service (similar to Sora) where model inference is a black box. How do you handle job submission, queuing, status tracking, and video retrieval?
GPU capacity is fixed and can't be elastically scaled on demand. How do you handle traffic bursts and keep GPU utilization high without dropping requests?
Why doesn't traditional autoscaling work for GPU-bound AI inference services, and what do you do instead?
How would you store and serve generated videos at scale? What's your approach to retention policies and CDN usage?
How do you handle failures mid-job, like a GPU node crashing during video generation? What are your retry semantics and how do you ensure idempotency?
What metrics and observability would you build into this system? What does a healthy system look like versus a degraded one?

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

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