Anthropic’s Enterprise AI Products team builds what makes Claude a daily-use tool for enterprise customers across industries. The role involves leading technical design, partnering with product, design, and go-to-market teams, setting technical direction, engaging with enterprise customers, collaborating with research to improve model capabilities, mentoring engineers, and building asynchronous agents. This is a technical leadership position with a focus on end-to-end product delivery and customer impact.
Staff+ Software Engineer, Enterprise AI Products na Anthropic
Híbrido - San Francisco, CA, United States
Ver mais vagas na AnthropicSalary
USD 405,000 - 485,000
Requirements
Skills
- 8+ years of software engineering experience, ideally with 2+ years at a Staff or equivalent technical leadership level
- Led 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
- 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
- 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 to turn enterprise customer workflows into shipped product, not just execute against a spec
- Set technical direction and standards for your team: architecture, code quality, and how the team builds
- Work directly with enterprise customers and sales during key conversations, translating what you learn into engineering priorities
- 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
- Mentor other engineers and raise the technical bar across the team, working with influence rather than authority
- Build multi-player, asynchronous agents: department-level processes that are goal-oriented, many-step, and triggered by a webhook, a form, or an email rather than a person typing
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