# What the Anthropic & OpenAI SWE interview loop is actually like (from 60+ reported accounts) > Source: > Published: 2026-06-25 00:42:55+00:00 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.*