Staff+ Software Engineer, Claude Science

Anthropic Anthropic · AI Frontier · San Francisco, CA · Engineering & Design - Product

Staff+ Software Engineer for Claude Science at Anthropic. This role involves building AI products that serve as a workbench for researchers, enabling them to conduct scientific work from hypothesis to publication. The engineer will partner with research teams to push model capabilities into production, shape product roadmaps, and translate user needs into engineering priorities. The role focuses on shipping AI-powered scientific tools and improving model performance for scientific tasks.

What you'd actually do

  1. 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
  2. Interface directly with working scientists — academic labs, industry R&D teams, and research institutes — during key conversations, translating what you learn into engineering priorities
  3. Partner with product and design to turn how scientists actually work — from hypothesis to analysis to publication — into shipped product
  4. 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

Skills

Required

  • 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
  • set technical direction with limited precedent
  • built AI products
  • turn model capabilities into applications people actually use
  • comfortable working directly with technical domain experts
  • translating what you learn
  • drive cross-team alignment to ship impactful work

Nice to have

  • Background in chemistry, biology, physics, or another science
  • Experience working with research teams to improve domain-specific model capabilities
  • evaluation frameworks

What the JD emphasized

  • built products from 0 to 1
  • built AI products
  • turn model capabilities into applications people actually use
  • shaping evals
  • evaluation frameworks

Other signals

  • building AI products
  • turn model capabilities into applications
  • partner directly with our internal research team to push model capabilities into production
  • carries real ownership over what we ship next
  • ship fast against a roadmap you help shape
  • interface directly with working scientists
  • translating what you learn into engineering priorities
  • work closely with research to make the models better at science
  • shaping evals
  • surfacing failure modes
  • feeding what users hit in the real world back into model development
  • built AI products and know what it takes to turn model capabilities into applications people actually use
  • improve domain-specific model capabilities, including evaluation frameworks