How OpenAI Interviews in 2026: Process, Questions & What They Score

1. 01

   Implement serialize/deserialize for a key-value store where keys and values may contain arbitrary characters (no delimiters or JSON libraries allowed): a length-prefix-encoding problem

   Tests binary-safe serialization; the insight is length-prefix encoding when delimiters and JSON libraries are banned.

2. 02

   Design and implement a versioned/time-based key-value store (record a version on each write; get the value as-of a given version or timestamp)

   Tests versioned key-value design: per-key version history with efficient as-of lookups, often binary search over versions.

3. 03

   Implement a spreadsheet-style cell system supporting formula references, cycle detection, and efficient (ideally O(1)) recomputation via a dependency graph

   Tests dependency-graph modeling for spreadsheet formulas: cycle detection and near O(1) incremental recomputation.

4. 04

   Implement a Unix-style path/'cd' resolver that handles '.', '..', absolute paths, and symlinks

   Tests path normalization: stack-based handling of '.', '..', absolute paths, and symlink resolution.

5. 05

   Build a resumable iterator that can pause and resume across multiple calls while preserving state

   Tests iterator state management: pausing and resuming traversal across calls without losing position.

6. 06

   Design a multithreaded web crawler that fetches pages concurrently while avoiding duplicate crawls

   Tests concurrency: thread coordination, a shared visited set under contention, and avoiding duplicate fetches.

7. 07

   Implement a small in-memory SQL-like engine (tables, inserts, selects with WHERE clauses, maybe joins)

   Tests in-memory query-engine design: table storage, WHERE filtering, and possibly joins under time pressure.

8. 08

   Solve a meeting-rooms / interval-scheduling problem

   Classic interval-scheduling problem; tests sorting by time plus a heap or sweep to count overlaps.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Design a system like Slack

   System design: they probe messaging fan-out, channel data models, presence, and real-time delivery at scale.

2. 02

   Design a global video/livestream platform

   System design: tests video ingest, transcoding, CDN distribution, and livestream latency tradeoffs.

3. 03

   Design GitHub Actions (or a GitHub-like system) from scratch

   System design: tests CI job orchestration: queueing, worker isolation, artifact storage, and failure handling.

4. 04

   Design a payment-processing system similar to Stripe

   System design: they probe idempotency, ledger consistency, retries, and exactly-once semantics for payments.

5. 05

   Design a ChatGPT-like conversational product end-to-end (or design the OpenAI Playground)

   System design: conversational product end to end: session state, model serving, streaming, and safety layers.

6. 06

   Design an online chess platform

   System design: tests real-time game state sync, matchmaking, and move validation for online chess.

7. 07

   Design a notification system at scale

   System design: they probe fan-out strategy, delivery guarantees, rate limiting, and user preferences at scale.

8. 08

   Design a large-scale, LLM-powered enterprise search system

   System design: tests retrieval architecture for enterprise search: embedding indexes, permissions, and LLM grounding.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   Implement a distributed all_gather-style collective operation across nodes

   Tests distributed-systems coding: implementing an all_gather collective across nodes with correct data movement.

2. 02

   Debug a broken or misbehaving transformer implementation

   Tests transformer internals via debugging: attention shapes, masking, initialization, and training instabilities.

3. 03

   Derive the RLHF objective both as KL-regularized reinforcement learning and as variational (Bayesian) inference, then discuss the practical trade-offs introduced by the KL term

   Tests RLHF theory: deriving the objective as KL-regularized RL and as variational inference, plus the KL term's tradeoffs.

4. 04

   Implement KL divergence (or another information-theory quantity) for continuous distributions

   Tests information-theory implementation: KL divergence for continuous distributions, done numerically stably.

5. 05

   Calculate the expected number of iterations for a given probabilistic process

   Probability exercise; tests setting up expectation calculations for an iterative random process.

6. 06

   Live-code a basic reinforcement-learning algorithm

   Tests practical RL knowledge by live-coding a basic algorithm such as policy gradient or Q-learning.

7. 07

   How would you deploy and monitor a large language model in production?

   Tests ML production knowledge: serving infrastructure, latency and cost tradeoffs, monitoring, and drift detection.

8. 08

   Discuss a research paper sent to you in advance, then discuss your own past research and where it might overlap with OpenAI's work

   Tests research depth: critically discussing an assigned paper and connecting your own work to OpenAI's directions.

Reported problems, listed so you know what to expect. Practice them in your own editor or on the platform you prefer; SupaCV's practice mode coaches how you talk through them.

1. 01

   You are responsible for releasing a new ChatGPT model. How would you plan and run the launch?

   Candidate-reported via Exponent, 4 candidates
2. 02

   You can generate music from written text. How would you build a product around that capability?

   Candidate-reported via Exponent, 2 candidates

1. 01

   OpenAI plans a shared team workspace inside ChatGPT. What success metrics would you set for it?

   Candidate-reported via Exponent, 2 candidates
2. 02

   OpenAI is building earbuds like AirPods with voice AI built in. What goals and success metrics would you set for them?

   Candidate-reported via Exponent, 2 candidates

1. 01

   You hold a technology that converts human speech or text into animal language. How would you bring it to market?

   Candidate-reported via Exponent, 2 candidates

1. 01

   Why do you want to work at OpenAI? (vague 'I love AI' or careers-page-quoting answers are repeatedly cited as a fast way to fail this)

2. 02

   What is your perspective on AGI safety and alignment?

3. 03

   Tell me about a time you acted as an owner of a problem, including when it went wrong

4. 04

   Describe your biggest failure and what you learned from it

5. 05

   Walk me through your most impactful project and your specific individual contribution

6. 06

   Tell me about a time you disagreed with a peer or manager and how you resolved it

7. 07

   Tell me about your most difficult product launch or cross-functional collaboration (e.g., working with legal or another non-engineering function)