I collected 28 OpenAI and Anthropic interview experiences. Here’s what stood out. · Blanked<br>The dataset<br>What the records can support<br>This comparison covers 15 OpenAI and 13 Anthropic public, self-reported candidate accounts collected for Blanked. The denominator changes when an account did not include usable detail for a particular field.
Experiences reviewed2815 OpenAI · 13 Anthropic<br>Practical preparation16/28Implementation, debugging or production constraints<br>Anthropic’s round cluster6/11Structured records reporting seven rounds
In this analysis<br>The core finding<br>What I looked at<br>Shared engineering emphasis<br>What practical meant<br>Preparing for OpenAI<br>Preparing for Anthropic<br>Round-count patterns<br>How I’d prepare<br>Blanked’s research sheet contains 15 OpenAI accounts and 13 Anthropic accounts. I reviewed them to answer a narrower question than the careers pages do: what did candidates say the interviews actually rewarded?<br>I expected the OpenAI accounts to be dominated by technical depth, while the Anthropic accounts would add a clear emphasis on mission and values. Practical engineering was prominent in both sets. The divergence appeared in the judgment around that work.<br>My read is that the engineering bar overlaps. In the OpenAI accounts, candidates repeatedly described being pushed on scale, failure modes and operational correctness. The Anthropic accounts contained those concerns too, but more often connected them to values, misuse and decisions made with incomplete information. That is the pattern I examine below.
01<br>What I looked at<br>I reviewed detailed process summaries, assessment themes, round counts and preparation advice from 28 public candidate accounts. For every finding, I used only the experiences containing the relevant detail and kept the supporting count visible.<br>That matters because these accounts are not uniform. One person may describe the assessment themes in depth but omit the total number of rounds; another may provide a round count without much preparation advice. Keeping the denominator beside each finding lets the interview fingerprints be compared without flattening different evidence into one generic score.<br>The records are not evenly distributed across sources Exponent supplied 18 of the 28 accounts. That concentration is important when interpreting a pattern that appears several times.
Exponent18<br>LeetCode5<br>Glassdoor3<br>Medium2<br>Of the 25 accounts with an interview year, 23 were from 2025 or 2026. I treat the results as recent, source-concentrated observations rather than estimates of the full candidate population.<br>How to read the numbers Experiences vary by role, level, location and year. OpenAI’s set also contains product, growth and data-science roles, while Anthropic’s is entirely software and ML. These counts are planning signals. They are not company-wide probabilities or a promise of what every candidate will encounter.<br>Read Blanked’s methodology →
02<br>Both sets emphasize practical engineering<br>I grouped the preparation advice into broad themes. I did not use exact interview questions or copy candidate wording.<br>Practical or production-focused preparation appeared in 10 of 15 OpenAI notes and 6 of 13 Anthropic notes. Combined, 16 of the 28 experiences pointed toward implementation, debugging, testing, production constraints or realistic engineering work.<br>Themes coded from the preparation advice Each bar shows the share of notes in that company’s set containing the named theme.<br>Practical or production-focused preparation<br>OpenAI10 of 15<br>Anthropic6 of 13
Theme that distinguished each set<br>OpenAI: systems or reliability7 of 15<br>Anthropic: mission or safety5 of 13
The lower pair contains two different codes; it is not a single scale with opposing endpoints. The comparison shows what recurred within each company’s preparation notes.<br>Algorithm drills alone would therefore be a weak preparation strategy. The data also does not support a technical-versus-cultural framing: technical work is common in both sets, while the preparation notes differ in what surrounds it.
03<br>What practical engineering meant in these accounts<br>The coding and design notes were more revealing than the stage names. I grouped the work formats without reproducing exact interview questions.<br>Work formats described in the accounts These are abstractions from the summaries, not a question bank.Accounts described multi-part implementation gated by tests, refactoring existing code and designing services across API boundaries. Follow-up discussion often moved into retries, failure modes, correctness and load.
Accounts described networked implementation, layered requirements, unfamiliar libraries and applied-data tasks. Several then moved into safety, misuse, ethics or trade-offs without a clean technical answer.
Both sets contained applied coding and system reasoning. In the OpenAI accounts, discussion more often moved toward production failure and scale. In the Anthropic accounts, it more often moved toward values, misuse...