Staff+ Software Engineer, Vertical AI Products (multiple Roles)

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

Staff+ Software Engineer for Anthropic's Vertical AI Products team, focusing on building industry-specific AI products (financial services, science, healthcare, enterprise) that integrate Claude. The role involves technical leadership, end-to-end product ownership, close collaboration with research, and direct customer engagement to shape and deliver AI solutions.

What you'd actually do

  1. Own technical design and delivery for a core piece of one of these vertical or enterprise products, end-to-end across the stack
  2. Work closely with research to make the models better in your domain — shaping evals, surfacing failure modes, and feeding customer learnings back into model development
  3. Partner with product, design, and go-to-market to turn enterprise customer workflows into shipped product, not just execute against a spec
  4. Set technical direction and standards for your team — architecture, code quality, and how the team builds
  5. Work directly with enterprise customers and sales during key conversations, translating what you learn into engineering priorities

Skills

Required

  • 8+ years of software engineering experience
  • 2+ years at a Staff or equivalent technical leadership level
  • led the design and delivery of complex enterprise or B2B products across the full stack
  • built AI products and know what it takes to turn model capabilities into applications people actually use
  • comfortable working directly with enterprise customers and translating what you learn into technical decisions
  • built products from 0 to 1 in fast-moving environments
  • can set technical direction with limited precedent to lean on
  • Drive cross-team alignment to ship impactful work, with influence over authority

Nice to have

  • Experience working with research to improve domain-specific model capabilities, including evaluation frameworks
  • Deep domain knowledge in one of these areas: investment banking, asset management, insurance, or corporate finance; scientific research or computational biology; clinical operations, health systems, or payers; or enterprise platform work
  • Exposure to both product-led growth and direct enterprise sales

What the JD emphasized

  • Staff+
  • enterprise customers
  • 0→1
  • customer workflows
  • technical leadership
  • shaping the product and the architecture
  • end-to-end customer experience
  • push model capabilities into production
  • ownership
  • enterprise customers
  • technical direction
  • technical bar
  • Staff or equivalent technical leadership level
  • complex enterprise or B2B products
  • built AI products
  • turn model capabilities into applications people actually use
  • enterprise customers
  • technical decisions
  • products from 0 to 1
  • limited precedent
  • cross-team alignment
  • influence over authority
  • domain knowledge

Other signals

  • building AI products purpose-built for specific industries
  • shaping the product and the architecture in markets where no one has done this well yet
  • products are already live with enterprise customers and growing fast
  • technical leader who thinks holistically about the end-to-end customer experience
  • partners directly with research to push model capabilities into production
  • carries real ownership over what we ship next