Staff+ Software Engineer, Enterprise AI Products

Anthropic•San Francisco, CA

22h•$405,000 - $485,000•Hybrid

About The Position

Anthropic's Enterprise AI Products team builds products that make Claude a daily-use tool for enterprise customers across industries. This team focuses on organizational context and workflows, including plugins, skills, connectors, and webhook-triggered processes/agents. The role involves shaping the product and architecture in a developing market, acting as a technical leader who thinks holistically about the end-to-end customer experience, partners with research to productionize model capabilities, and owns shipped products. The work is largely 0→1, meaning new product development in an area where best practices are still emerging.

Requirements

8+ years of software engineering experience.
Ideally 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 understand how to turn model capabilities into usable applications.
Comfortable working directly with enterprise customers and translating learnings into technical decisions.
Experience building products from 0 to 1 in fast-moving environments.
Ability to set technical direction with limited precedent.
Drive cross-team alignment to ship impactful work, with influence over authority.

Nice To Haves

Experience working with research to improve domain-specific model capabilities, including evaluation frameworks.
Experience building extensibility surfaces (plugins, integrations, agent tooling) that third parties or internal teams build on.
Exposure to both product-led growth and direct enterprise sales.

Responsibilities

Own technical design and delivery for enterprise-facing core products, end-to-end across the stack.
Partner with product, design, and go-to-market teams to translate enterprise customer workflows into shipped product.
Set technical direction and standards for the team, including architecture, code quality, and build processes.
Work directly with enterprise customers and sales during key conversations, translating learnings into engineering priorities.
Collaborate closely with research to improve models in the enterprise domain, shaping evaluations, identifying failure modes, and feeding customer learnings back into model development.
Mentor other engineers and raise the technical bar across the team through influence.
Build multi-player, asynchronous agents for department-level processes that are goal-oriented, multi-step, and triggered by webhooks, forms, or emails.