← Back to Directory

OpenAI interview notes, page 65 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

Jul 2026
Went through a technical phone screen for an ML Engineer role at OpenAI that was pretty heavy on statistics and Bayesian inference. The whole thing felt like a grad-level probability exam more than a typical ML interview, which I wasn't fully expecting.
  • Given n Bernoulli trials with k successes, derive the maximum likelihood estimator for the success probability p and describe its asymptotic variance.
  • With a Beta(alpha, beta) prior on p, derive the posterior distribution and compute the posterior predictive probability of success on the next trial.
  • Construct a 95% confidence interval for p using a normal approximation, and separately a 95% credible interval from the posterior. Walk through both.
  • When is each type of interval actually reliable, and how does sample size change the picture for both frequentist and Bayesian inference?

“The MLE derivation itself is straightforward, k/n, anyone who's taken a stats course gets there.”

View Post

OpenAI·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at OpenAI focused entirely on building a ChatGPT-style product end to end. Pretty intense scope for a single session, covering frontend, backend, streaming protocols, and operational concerns all at once.
  • Design the frontend and backend architecture for a ChatGPT-style homepage from scratch.
  • Compare Server-Sent Events and WebSockets for streaming tokens to the browser. What are the trade-offs in latency, reliability, backpressure, reconnection behavior, and browser support?
  • How would you integrate a Chat Completions API, including authentication, rate limiting, conversation state storage, and streaming tokenization?
  • Walk through your error handling and retry strategy for a streaming inference pipeline.
  • How would you approach observability for this system, covering logs, metrics, and distributed tracing?
  • What are the scalability and cost considerations for a system like this at production scale?

“I started with the UI layer and worked backwards, which felt natural but I think I spent too long on component structure before touching the backend.”

View Post

OpenAI·Software Engineer·Onsite - System Design / Architecture

Jul 2026
System design round at OpenAI for a SWE role, centered entirely on designing Slack from scratch. Dense interview covering a lot of ground, from basic DM delivery all the way up to sharding and multi-tenancy.
  • Design Slack: covering direct messages, channels, multi-device delivery, notifications, file sharing, message deletion, and large-scale fan-out with sharding.
  • When would you switch from a per-user fan-out model to a per-channel fan-out model, and why?
  • Can you unify the push and pull delivery models into a single pull-based approach?
  • How do you handle multi-device sync so that reading a message on one device clears it across all a user's devices?
  • How would you design multi-tenancy for an enterprise chat system with strict workspace isolation?
  • How do you generate message IDs that are globally unique, time-sortable, and don't require a central lock?
  • How does a client recover missed messages after a reconnect, and how do you avoid a thundering herd when many clients reconnect at once?

“This is basically a full system design marathon in one question.”

View Post

OpenAI·Software Engineer·Technical Phone Screen

Jul 2026
Got a general coding question for a SWE role at OpenAI that looked deceptively simple on the surface. The problem involved generating sequences with some prefix-balance constraints across three tags, and it took me longer than I'd like to admit to really internalize what the constraints were asking.
  • Given three tags (AI, Human, Task) and three allowed pairs (AI-Human, AI-Task, Human-Task), generate a sequence of N pairs such that for every prefix of the sequence, the absolute difference in occurrence counts between AI and Human is at most 1, and the absolute difference between AI and Task is also at most 1.

“I stared at this for a bit trying to figure out if there was a cycle pattern that just works.”

View Post

OpenAI·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Interviewed for an ML Engineer role at OpenAI and got hit with a grid simulation problem that felt more like a competitive programming warmup than anything ML-related. Interesting problem but I kept second-guessing my BFS approach the whole time.
  • Given an n x m grid where cells are either infected or healthy, and infection spreads in discrete time steps (a healthy cell becomes infected if it has at least K infected neighbors), return the minimum number of time steps until the entire grid is infected, or -1 if it's impossible.

“My first instinct was multi-source BFS and that direction was right, but K not being 1 threw me off.”

View Post

OpenAI·Software Engineer·Technical Phone Screen

Jul 2026
OpenAI SWE technical screen involving a PyTorch debugging and implementation problem. The problem was meaty enough that it felt more like a take-home compressed into a live session.
  • You're given a broken miniGPT implementation in PyTorch. Find and fix the bugs in the forward pass, attention mechanism, causal masking, positional embeddings, logits computation, and decoding logic so the model generates correct text.
  • As a follow-up, implement a KV cache for autoregressive decoding. Keys and values should be cached per layer, new tokens should only compute K/V for themselves, and the output must match the non-cached version under the same seed.

“This was the bulk of the session.”

View Post

OpenAI·Software Engineer·Technical Phone Screen

Jul 2026
Interviewed for a software engineering role at OpenAI and got hit with a deeply technical ML debugging question about a churn prediction pipeline. One question, but it was a lot to unpack.
  • You're given a binary churn prediction pipeline where training AUC is 0.95, random-split validation AUC is 0.62, and a time-based holdout AUC is 0.55. Predicted probabilities are overconfident and the positive class is heavily imbalanced at 1:10. Walk through how you'd debug this end to end, covering data leakage, label and feature drift, cross-validation design, error analysis, feature ablations, and training fixes like regularization and resampling. Also explain how you'd verify improvements and prevent future regressions.

“This question is a lot.”

View Post

OpenAI·Software Engineer·Onsite - Coding / Algorithms

Jul 2026
OpenAI SWE coding round, one meaty problem the whole time. The question was a distributed rate limiter with persistence and fallback behavior, and it went deeper than I expected with the follow-ups.
  • Design and implement a distributed rate limiter that supports multiple app servers sharing state, with persistence and fallback to local storage when the shared store is unavailable.

“This started feeling manageable when I picked sliding window log with Redis sorted sets, but the follow-ups are where things got uncomfortable fast.”

View Post

OpenAI·Software Engineer·Onsite - System Design / Architecture

Jul 2026
OpenAI system design round focused entirely on building a RAG-based enterprise assistant, and the depth they expected on the ML side caught me off guard. Not your usual 'draw boxes and arrows' design question.
  • Walk through an end-to-end RAG system for an internal enterprise assistant. How do you break it into components, and what are the core ML choices at each stage?
  • For the retriever component, what model architecture would you use and how would you train it, including loss functions and how you'd handle negative examples?
  • How would you train the reranker, and what data labeling strategy would you use given that internal document relevance labels are expensive to get?
  • How does your system handle document-level permissions so that a user never sees content from documents they don't have access to?
  • What are the main failure modes in a RAG system like this, and how do your modeling and evaluation choices address them?
  • How would you evaluate this system offline and in production, and what metrics matter most for each stage?
  • How would you handle very long documents that don't fit in a single context window for either retrieval or generation?

“This is where I spent most of the interview.”

View Post

OpenAI·Machine Learning Engineer·Technical Phone Screen

Jul 2026
OpenAI MLE interview centered on a machine learning problem about handling noisy annotators in a training pipeline. The format varied by round but the core technical content stayed consistent, and the emphasis was less on fancy methods and more on code structure and how you reason through tradeoffs.
  • You have a classifier trained on labels from multiple human annotators, some of whom are noisy. How do you identify the bad annotators, remove their labels, and verify whether the classifier actually improves?
  • Given a 400+ line PyTorch file implementing this classifier pipeline, walk through the code, identify structural issues, and refactor it into cleaner components.
  • How would you handle the bias-variance tradeoff when filtering annotator labels, given that the input dimensionality is high and you can't afford to lose too much data?

“This is the main question and it sounds straightforward until you actually have to code it up.”

View Post