Anthropic is a public benefit corporation focused on building reliable, interpretable, and steerable AI systems. The Claude Science team is developing an AI workbench for researchers, integrating literature, scientific computing, and analysis tools. As a Staff+ Software Engineer, you will lead technical design and product development, collaborate with scientists and internal research teams to translate domain needs into engineering priorities, and help shape model capabilities into usable applications across biology, chemistry, physics, and more.
Staff+ Software Engineer, Claude Science en Anthropic
Híbrido - San Francisco, CA
Más vacantes en AnthropicSalary
USD 405,000 - 485,000
Requirements
Skills
- Have 8+ years of software engineering experience, ideally with 2+ years at a Staff or equivalent technical leadership level
- Have built products from 0 to 1 in fast-moving environments, and can set technical direction with limited precedent to lean on
- Have built AI products and know what it takes to turn model capabilities into applications people actually use
- Are comfortable working directly with technical domain experts and translating what you learn
- Drive cross-team alignment to ship impactful work, with influence over authority
- Background in chemistry, biology, physics, or another science
- Experience working with research teams to improve domain-specific model capabilities, including evaluation frameworks
Responsibilities
- Ship fast against a roadmap you help shape: this is a product in a category no one has defined yet, and the highest-leverage problems are still unclaimed
- Interface directly with working scientists — academic labs, industry R&D teams, and research institutes — during key conversations, translating what you learn into engineering priorities
- Partner with product and design to turn how scientists actually work — from hypothesis to analysis to publication — into shipped product
- Work closely with research to make the models better at science: shaping evals, surfacing failure modes, and feeding what users hit in the real world back into model development
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