The process typically includes: recruiter screen (30 min), technical phone screen with coding (45-60 min), take-home or live coding challenge focused on systems or ML infrastructure (2-4 hrs), a full onsite (virtual) loop with 4-5 rounds covering coding, system design, technical leadership, and an AI safety alignment discussion. Final round may include a conversation with a senior researcher.

Anthropic primarily uses Python for ML research and experimentation, C++ for low-level model training infrastructure, and increasingly Rust for safety-critical systems components. TypeScript/React is used for internal tooling and Claude.ai. Engineers should be proficient in Python and comfortable with at least one systems language.

Expect at least one round specifically focused on your understanding of and commitment to AI safety. Interviewers want to understand how you reason about potential failure modes of AI systems, how you approach building safety mechanisms into software, and your views on responsible AI development. This isn't a gotcha round — it's a conversation about values alignment.

Common system design topics include: large-scale distributed training pipelines, inference serving at scale, data pipelines for training data curation, monitoring and observability systems for model behavior, and developer API infrastructure. Expect questions that blend classical distributed systems knowledge with ML-specific constraints.

Anthropic SWE compensation (2025 data): L4 (mid-level): $180k-$250k base, $300k-$500k total; L5 (senior): $230k-$320k base, $500k-$900k total; L6 (staff): $280k-$380k base, $700k+ total. Compensation includes base salary, equity with significant upside, and strong benefits. Mission alignment is a key factor in offer conversations.

Practice LeetCode medium/hard problems focusing on arrays, graphs, dynamic programming, and system design. More importantly, practice writing clean, well-tested, production-quality code — Anthropic interviewers pay close attention to code structure and readability. Brush up on distributed systems fundamentals and be ready to discuss performance trade-offs in ML infrastructure contexts.

Yes, Anthropic hires new graduates and recent PhD graduates, particularly for research engineering and infrastructure roles. University hire interviews follow a similar structure but may place more emphasis on internship projects and academic work. New grad roles require demonstrated technical ability and genuine interest in AI safety.

Engineering and research teams at Anthropic are tightly integrated. Engineers regularly attend research meetings, contribute to experiment design, and implement novel ideas directly from papers. This is not a typical product engineering role — you'll need intellectual curiosity and comfort navigating ambiguity. Many engineers co-author research papers.