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OpenAI interview notes, page 55 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·Mobile Engineer·Technical Phone Screen

Jun 2026
Interviewed for a mobile engineer role at OpenAI and got a Jetpack Compose UI question that was more involved than I expected. Basically had to rebuild the Google Translate home screen from scratch, with real attention to state management and composable structure.
  • Build an Android UI using Jetpack Compose that replicates the Google Translate home screen, including language selectors with a swap button, a text input area, a translated text display, and a bottom row of action icons. Use proper state management, reusable composables, and Material 3 styling.

“I spent the first few minutes just thinking about composable decomposition before writing anything.”

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OpenAI·Backend Engineer·Technical Phone Screen

Jun 2026
OpenAI backend interview with a deep ML systems debugging question. They handed me a broken Transformer implementation and wanted me to find every bug, fix it, and explain the reasoning. Pretty intense for a backend role but I guess that's what you sign up for there.
  • You're given a buggy Transformer implementation (could be multi-head attention, positional encoding, encoder/decoder block, etc.). Find all the bugs, fix them, and explain why each fix is correct. Expect follow-up questions about extending or training the model.

“The part that got me was the masking.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
Coding round at OpenAI for a software engineer role. One question, but it had enough moving parts to trip you up if you went in assuming it was trivial.
  • Design an encode and decode function pair for a list of strings. The encoding must handle arbitrary Unicode input without relying on any special delimiter characters.

“My first instinct was to just join with some separator character, which is exactly the wrong move.”

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OpenAI·Backend Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a backend engineer role. The whole thing was focused on one big problem: designing a webhook delivery service end to end. Dense topic with a lot of moving parts, and I felt like I was constantly playing catch-up as the scope kept expanding.
  • Design a webhook delivery system where customers can register HTTP endpoints to receive event notifications, with at-least-once delivery, signed payloads, retries with exponential backoff, a dead-letter queue, and tenant isolation.
  • How would you design the worker pool and queue structure to support high throughput while maintaining ordering guarantees per subscription?
  • How would you handle consumer-side idempotency so that retried webhook deliveries don't cause duplicate processing on the receiving end?
  • Walk through your approach to securing webhook payloads, including signature verification and rate limiting per tenant.

“This was basically the whole interview.”

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OpenAI·Mobile Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a mobile engineer role. The question was one big sprawling prompt about quota handling for a GPT chat product, which sounds focused until you realize it touches API contracts, client UX, content-type billing, and edge cases all at once.
  • Design the full error-handling flow for when a free-tier user hits their message quota on a GPT chat service. This includes the API contract (HTTP status, error codes, retry-after headers, upgrade hints), how the server tells quota exhaustion apart from rate limiting, idempotency, client-side UI behavior, how different content types (text, image, voice, file) consume quota differently, and edge cases like mid-stream responses getting cut off.

“This felt like four questions stapled together and I kind of panicked at the scope.”

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OpenAI·Machine Learning Engineer·Take-home Assignment

Jun 2026
Took a take-home style ML engineering screen for OpenAI that was basically a full mini-project: build a classifier end-to-end, do the EDA, handle imbalance, tune models, explain your choices, and write it all up. Dense scope for a single assignment.
  • Given a labeled dataset, build a complete end-to-end classifier pipeline in Python: perform exploratory data analysis including schema validation, missing value handling, target leakage checks, class imbalance assessment, and feature distributions; create appropriate train/validation/test splits; define a baseline; train at least two models with cross-validation and hyperparameter tuning; handle class imbalance; apply preprocessing; evaluate with business-aligned metrics; run error analysis with feature importances or SHAP; check fairness across subgroups; and deliver a reproducible report with your recommended model.

“This was the whole assignment, not just one question.”

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OpenAI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a software engineering role. The whole thing was basically one meaty problem about building a credit management system for GPU usage, and they wanted both design and working code.
  • Design and implement a GPU credit management system with three operations: creating a credit grant with an amount and expiration time, subtracting credit at a given timestamp (deducting from earliest-expiring grants first), and querying the available balance at any arbitrary timestamp including past ones.

“This was harder than I expected because it's not just a balance tracker.”

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OpenAI·Software Engineer·Technical Phone Screen

Jun 2026
OpenAI SWE interview centered on a scheduling problem for a data labeling platform, with multiple follow-up parts that got progressively harder. The core question is apparently making the rounds right now and caught a lot of people off guard on the prefix-balance constraint.
  • Design a scheduler for a data labeling platform with t tasks, m models, and h human labelers. Produce a schedule as a list of (task, model, human) assignments where each human participates in at least k tasks and each (task, human) pair appears at most once.
  • Extend the scheduler so that for every prefix of the output schedule, the difference between the maximum and minimum number of times any model has been assigned to a given task is at most 1. Same condition must hold for human labelers per task.
  • Further extend the balanced scheduler so that for each human labeler, the distribution of models they are paired with is also as even as possible across all (model, human) combinations.
  • Adapt the scheduler to a streaming setting where new tasks arrive each day, the schedule must be updated incrementally without restarting from scratch, and each human can handle at most one task per day.

“Part 1 felt manageable.”

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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 a task scheduling system for a human data-labeling platform, which sounds deceptively manageable until you get into the incremental update requirements and the fairness constraints across both humans and models.
  • Design a task scheduling system for a human data-labeling platform. Each day a new batch of tasks arrives and must be assigned to human labelers and models, with constraints around fairness, no repeat assignments, and incremental daily updates rather than full recomputation.

“The fairness angle is what trips you up.”

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OpenAI·Backend Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at OpenAI for a backend engineer role, focused entirely on payment infrastructure. The question was dense and covered a lot of ground, probably more than I could fully address in one session.
  • Design a payment processing system that handles authorize, capture, and refund flows for card payments at high transaction volume. Requirements include multi-currency support, idempotency on retries, fraud screening, strong consistency on balances, exactly-once semantics for user-visible operations, an audit log, and PCI compliance. Walk through the ledger data model, how you'd integrate with payment service providers and card networks, async settlement, and monitoring.

“This is one of those questions where you can tell within the first two minutes you're not going to cover everything.”

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