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OpenAI interview notes, page 3 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·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Interviewed for an ML Engineer role at OpenAI and got a simulation-style coding problem that felt more like a grid puzzle than anything ML-specific. Short session, one meaty question.
  • Given a grid where infected cells spread each day and recover after D days (becoming immune), return the number of days until the grid fully stabilizes with no remaining infected cells.

“Took me a minute to figure out what 'stabilizes' actually meant since immune cells block further spread.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
ML coding round at OpenAI for an MLE role. The whole thing was a debugging exercise on a broken Transformer implementation, which sounds manageable until you're staring at someone else's messy code trying to remember whether pre-LN or post-LN even matters.
  • You're given a buggy Transformer implementation. Find all the bugs, explain why each one is wrong, and fix them. Issues may include attention masking, scaling, residual connections, tensor shapes, dropout, or positional encoding.

“This is genuinely hard under pressure.”

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OpenAI·Backend Engineer·Onsite - System Design / Architecture

Apr 2026
System design round at OpenAI for a backend role. The whole session was basically one giant calendar design question that branched into like five different sub-problems. Harder than I expected, mostly because the scope kept expanding.
  • Design a calendar service similar to Google Calendar or Apple Calendar, supporting event creation, editing, deletion, recurring events, invitations with RSVPs, reminders, multi-calendar overlays, and time zone handling.
  • How would you model recurring events with exceptions, such as skipping or modifying a single occurrence in a repeating series?
  • Walk through how you'd handle conflict detection when multiple users or devices try to update the same event simultaneously.
  • How would you design push-based sync across multiple devices, similar to how CalDAV works, while supporting offline-first behavior?
  • How would you architect the notifications and reminders system to scale to billions of events?

“The surface area of this question is deceptive.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026
OpenAI software engineer interview with a coding problem that's basically a simulation of a credit/balance system. Pretty niche problem, felt like it was testing more than just coding chops.
  • Design and implement a credit/balance system that supports adding and subtracting credits. Subtraction requests should be processed in order, but any request that would exceed the current balance gets skipped. Return the final balance and the list of executed transactions.

“The skip-not-fail part is what tripped me up at first.”

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OpenAI·Software Engineer·Technical Phone Screen

Apr 2026
Got a coding screen at OpenAI that was basically one problem: build an iterator over all IPs in a CIDR block. Seemed niche at first but the bitwise mask stuff is where they actually wanted to see you think.
  • Implement a Python iterator class that yields every IP address in a given CIDR block, such as '192.168.1.0/24'.

“The iterator protocol part was fine, __init__ __iter__ __next__, no surprises there.”

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OpenAI·Product Manager·Recruiter / HR Screen

Apr 2026
Interviewed for a PM role at OpenAI. Just one question to go off here, but it was the kind of values-alignment thing you'd expect early in the process.
  • Which aspects of OpenAI's mission resonate most with you?

“I rambled a bit about AI safety and beneficial outcomes for humanity, which felt genuine but also kind of obvious.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Got a pretty focused ML interview at OpenAI, at least the one question I remember was a real head-scratcher about streaming inference. Short but not easy.
  • If logits are arriving block-by-block in a streaming fashion, how would you compute entropy online without waiting for all of them?

“This one took me a second.”

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OpenAI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
ML coding round at OpenAI focused entirely on KV caching for Transformer inference. Pretty deep technically, more systems-aware than a typical coding screen.
  • Implement KV caching for a multi-head self-attention layer. At each generation step, append the new token's key and value to a per-layer cache and attend over the full cached sequence instead of recomputing K and V for past tokens.

“The core implementation wasn't the hard part.”

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OpenAI·Software Engineer·Recruiter / HR Screen

Apr 2026
Interviewed for a software engineer role at OpenAI, basically just an intro screen where they asked me to walk through my background. Pretty standard stuff, nothing technical yet.
  • Walk me through your background, including your current role, notable projects, and why you're interested in this opportunity.

“Rambled a bit connecting my projects to why I wanted the role specifically.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Apr 2026
OpenAI Research Engineer interview with a search specialist who clearly knew their stuff. The whole session revolved around designing a RAG system, but the real grilling was on embedding model internals, way deeper than I expected for a system design round.
  • Design an end-to-end Retrieval-Augmented Generation (RAG) system, covering indexing, retrieval, reranking, and generation.
  • How is a text embedding model trained? Walk through the contrastive learning setup.
  • Which loss function would you use for training an embedding model, and why? Compare InfoNCE, triplet loss, and cosine similarity loss.
  • How do you construct positive training samples for an embedding model? What are the tradeoffs between in-batch positives, hard mining, and data augmentation?
  • How do you sample negatives during embedding model training? Discuss random in-batch negatives, hard negatives, and ANN-mined negatives, and how many negatives per query you'd use.

“Started okay with the high-level pipeline but the interviewer kept pushing past the surface stuff.”

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