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Browse independent practice aligned to OpenAI: coding, system design, and project practice. Rehearse progressive coding, research-engineering system design, AI infrastructure, inference APIs, and LLM evaluation through original work-like exercises.
Use these reports to choose what to rehearse, not to predict a fixed loop. Formats vary by role, team, and date; confirm your current process with the recruiter. Frontier Rehearsal is independent and not company-endorsed.
Practice prompt (not a reported interview question): choose a practical developer tool or data structure, implement a usable core in 45 minutes, then diagnose one failure and add a feature while keeping the design testable.
Open permanent linkPractice prompt (not a reported interview question): build a small working service from an incomplete specification. State your assumptions, ship the simplest correct version, then explain how you would test and extend it when the requirements change.
Open permanent linkPractice prompt (not a reported interview question): implement a stateful component in stages. After each working version, handle a new requirement without breaking earlier behavior, and explain the trade-offs behind each revision.
Open permanent linkAccording to candidate reports, OpenAI coding rounds are multi-part exercises that start simple and layer on complexity, while system-design discussions have covered retrieval-augmented chatbots, payment flows and chat-system architecture.
Open permanent linkCandidates report that the OpenAI MLE phone screen presents two problems, each split into several parts that must be completed within the call; interviewers are sometimes willing to overlook an edge-case failure when the overall approach is sound.
Open permanent linkAccording to a candidate report, OpenAI offered a choice between a traditional 60-minute coding screen and a 60-minute agentic-coding screen in which AI tools are allowed and the task is to navigate, plan and validate changes in an unfamiliar Python codebase.
Open permanent linkCandidate reports describe the OpenAI hiring-manager screen as a management-skills interview driven by past-experience behavioural questions, run at a brisk pace with little introductory conversation.
Open permanent linkAccording to candidate reports, a failed OpenAI interview typically leads to a cooldown of roughly a year, and repeat candidates are given lower priority than first-time applicants.
Open permanent linkAccording to a candidate report, OpenAI onsite loops for machine-learning roles can include two separate ML coding rounds.
Open permanent linkCandidate reports indicate that the OpenAI phone screen pairs a coding exercise with a system-design exercise, and that recruiters have invited a candidate to redo the system-design portion after a strong coding signal.
Open permanent linkCandidate reports say OpenAI and Anthropic favour practical coding over LeetCode-style algorithm puzzles, and that behavioural or culture rounds test alignment with company values; examples cited include building a small SQL engine or a key-value store with disk persistence.
Open permanent linkCandidates report that the OpenAI virtual onsite has four rounds in total, one of which is coding, and that a weak single round is sometimes weighed against the rest of the packet rather than treated as an automatic rejection.
Open permanent linkAccording to candidate reports, the OpenAI first-round screen combines a system-design segment with a coding segment, and the coding prompt arrives with unusually long instructions even though the underlying task is fairly standard.
Open permanent linkCandidates report that OpenAI onsite dates are booked through a self-service tool that limits how far ahead slots can be reserved, with some candidates emailing the recruiter to request later dates; a preparation window of about three weeks is commonly discussed.
Open permanent linkCandidate reports indicate that OpenAI product-manager interviews include separate product-sense and product-execution rounds.
Open permanent linkCandidate reports describe OpenAI's phone-screen bar as demanding: coding answers are expected to complete every part almost flawlessly, and system-design answers are expected to justify design choices in depth.
Open permanent linkCandidate reports describe the OpenAI SWE loop as a phone screen with a 60-minute coding round and a 60-minute system-design round, then a virtual onsite adding a further coding round, a further system-design round, a technical deep dive and a behavioural interview; coding prompts are often applied builds such as a key-value store with time-based expiry or a rate limiter.
Open permanent linkCandidates describe OpenAI and Anthropic onsites as a mix of coding, system design and behavioural or project-deep-dive conversations, and note that strong technical rounds alone may not carry an offer when the behavioural and ownership signals are weak.
Open permanent linkEvidence reviewed . Reports can become stale.
Every question has a public brief with the task, constraints, and follow-ups. Labels describe interactive practice access.
Reported interview periodN/A
Design a sequence that stays fast to index, accepts mutation, and can crash at any point without losing the state a reader already saw. Then make the restart path provable rather than plausible.
Practice focusData structure design, Complexity analysis, Durability, Crash recovery through a written response exercise.
Reported interview periodN/A
The per-token price dropped to one fifth of its previous level while the prompt sets and reported traffic held steady, yet the monthly bill climbed. Build the account of where the money went, then defend the one fix you would ship.
Practice focusCost attribution, Experiment design, Token accounting, Root-cause analysis through a written response exercise.
Reported interview periodN/A
One session can carry hours of streamed output and far more history than the DOM should hold. Choose the transport, define what a resume means, and keep reading position and memory under control.
Practice focusStreaming protocols, Resume semantics, Incremental rendering, Virtualization through a written response exercise.
Reported interview periodN/A
Two people edit one note and need to see who wrote the last line.
Practice focusConflict, Failure through a written response exercise.
Reported interview periodN/A
Store a score per model run and show which run is ahead this week.
Practice focusRead path, Write path through a written response exercise.
Reported interview periodN/A
A story about slowing a launch because a safety check was thin.
Practice focusJudgment, A specific story through a written response exercise.
Reported interview periodN/A
A disagreement about training on customer text.
Practice focusDisagreement, Limits through a written response exercise.
Reported interview periodN/A
Two minutes on why this lab's work fits a project you have done.
Practice focusA concrete project through a written response exercise.
Reported interview periodN/A
What you would cut if the team lost half its people.
Practice focusPriorities, A hard conversation through a written response exercise.
Reported interview periodN/A
Where a design failed after launch, what you measured, and what you changed.
Practice focusA shipped system, Measurement through a written response exercise.
Reported interview periodN/A
Explain one result you trust, what would falsify it, and what was not shown.
Practice focusEvidence, Limits through a written response exercise.