Staff Software Engineer (Applied AI, Science & Engineering)

Join Anthropic, a leading AI safety and research company, as a Staff Software Engineer in the Applied AI, Science & Engineering team. In this role, you will work closely with domain experts to apply AI to real-world scientific and engineering problems. You will build the tools and systems that enable AI to drive research and engineering work, and you will have the opportunity to shape how AI is applied to science and engineering.

• Construire des systèmes qui permettent à Claude de résoudre des problèmes de recherche, de simulation et d'ingénierie.
• Connecter des outils de calcul scientifique et de simulation pour que Claude puisse les piloter de manière fiable et à grande échelle.
• Collaborer avec des scientifiques et des ingénieurs pour comprendre leurs problèmes et transformer des questions ouvertes en tâches vérifiables.

• Bring high agency, pick up new domains quickly, and hold strong opinions loosely
• Communicate clearly with researchers, engineers, and external partners, and care about the societal impacts of your work
• Have a track record of zero-to-one work in startup or startup-like environments\
• Have worked on technical problems in a science or engineering field, such as physics, materials, chemistry, or electrical or mechanical engineering
• Can work directly with expert users, understand their workflows deeply, and still keep the focus on building the right system rather than the one first requested
• Have 8+ years of experience building software, with strong engineering fundamentals and comfort across the stack, from data pipelines to tooling to quick interfaces
• Have built real systems with large language models, including agents, tool use, or evaluations
• Have good judgment about what can and can't be verified, and are comfortable saying a result isn't good enough yet
• We encourage you to apply even if you do not believe you meet every single qualification
• An advanced degree or research experience in a physical science or engineering field
• Hands-on experience with scientific computing and simulation, numerical methods, or optimization
• Experience in industrial R&D, a national lab, or a deep-tech company in areas like energy, materials, manufacturing, or hardware
• Experience with ML research, RL environments, or evaluation design
• Experience building alongside customers or technical partners, for example in forward-deployed, applied AI, or solutions engineering roles, ideally where you also owned the software that came out of it