What the Anthropic & OpenAI SWE interview loop is actually like (from 60+ reported accounts)

An engineer synthesized over 60 publicly-reported first-hand accounts to compare the software-engineering interview loops at Anthropic and OpenAI. The analysis reveals that both companies have similar stage structures but differ in emphasis: Anthropic integrates values-awareness from the recruiter screen and includes a universal values round that often filters out technically strong candidates, while OpenAI front-loads team fit. Key technical preparation includes building data structures like in-memory key-value stores and LRU caches from scratch in Python, and candidates are advised to lead discussions and bake safety into designs.

Independent and unofficial. Synthesized from publicly-reported, first-hand candidate accounts 2024–2026 . Not affiliated with, authorized by, or endorsed by Anthropic, OpenAI, or any company named. Treat stage structure as well-corroborated and all numbers as directional self-report. I've been collecting publicly-reported, first-hand accounts of the software-engineering interview loops at Anthropic and OpenAI . The patterns are consistent enough to be worth writing down. The two loops rhyme but emphasize different things — Anthropic is values-aware from the recruiter screen; OpenAI front-loads team fit. The single most consistent finding: a values / culture round appears in essentially every Anthropic onsite , and it fails more technically-strong candidates than any coding round. | Stage | Anthropic | OpenAI | |---|---|---| | Recruiter screen | Mission/values-aware from minute one | Background + which team is hiring | | First technical filter | CodeSignal OA, ~4 progressive levels often waived for referrals/seniors | CoderPad/HackerRank screen, or a 4–8 hr take-home | | Onsite | ~4–6 rounds: coding, system/AI-infra design, values universal , deep-dive | ~3–5 rounds: coding, system design, refactoring senior , deep-dive, behavioral | | Design tool | Shared Google Doc | Excalidraw | | After | References + team matching opaque | Hiring committee + org match | | Negotiation | Expected | Tends to hold firmer | Be fluent building these from scratch in Python a real edge : an in-memory multi-level key-value store, a web crawler, an LRU cache, a stack-trace / sampling-profiler problem, a tokenizer, a distributed mode/median exercise. Knowing them is table stakes; surviving the perturbation is the test. Almost verbatim across sources: do the math first; design the simplest system that meets the stated numbers; bake safety/limits into the request flow; lead the discussion yourself. Anthropic prompt themes are infra-shaped serving LLMs, token services, retrieval, agents ; OpenAI leans more product-shaped. It's reflective and probing — "a time your values were tested," "a belief you changed," "a genuine critique of the company." Follow-ups probe your reasoning and honesty , not tidy outcomes. Candidates who pass build a few true stories only they could tell, form a real point of view on AI safety, and read the primary sources Core Views on AI Safety, the Responsible Scaling Policy, Dario Amodei's essays to engage critically — not memorize. I compiled the full ~105-page version — the master question bank, the values-round playbook, reconciled comp data, and a prep plan — grounded in the same 60+ accounts. The condensed field analysis is free here, and there's a free cheat-sheet on GitHub.