Staff+ Software Engineer, Claude Science

Anthropic•San Francisco, CA

19h•Hybrid

About The Position

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. Claude Science is an AI workbench that gives researchers a single environment for work that today spans dozens of disconnected tools — literature, specialized databases, scientific computing, analysis, and publication-ready outputs. Claude Science already renders protein structures, genome tracks, and chemical structures natively, and coordinates multi-agent workflows with built-in review for citation and calculation errors. This role involves shaping both the product and the architecture in a category no one has defined yet. The engineer will be a technical leader who thinks holistically about the end-to-end researcher experience, partners directly with the internal research team to push model capabilities into production, and carries real ownership over what is shipped next. The north star is accelerating scientific progress — across biology, chemistry, physics, and beyond — from early discovery through real-world application, by an order of magnitude. This work supports programs like Anthropic’s AI for Science Program and rare disease research grants.

Requirements

8+ years of software engineering experience.
2+ years at a Staff or equivalent technical leadership level.
Built products from 0 to 1 in fast-moving environments, and can set technical direction with limited precedent to lean on.
Built AI products and know what it takes to turn model capabilities into applications people actually use.
Comfortable working directly with technical domain experts and translating what is learned.
Drive cross-team alignment to ship impactful work, with influence over authority.
Bachelor’s degree or an equivalent combination of education, training, and/or experience.
A field relevant to the role as demonstrated through coursework, training, or professional experience.

Nice To Haves

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 that the engineer helps shape.
Interface directly with working scientists — academic labs, industry R&D teams, and research institutes — during key conversations, translating what is learned 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.