Engineering Manager, Enterprise

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

Engineering Manager for Anthropic's Enterprise pillar, responsible for making Claude enterprise-ready at scale. This role focuses on building foundational systems for large organizations, removing deployment blockers, and ensuring security, compliance, and control. The manager will lead a team, own engineering execution, partner with product, design, sales, and customer success, and shape the roadmap.

What you'd actually do

  1. Lead and develop a team of engineers building out features and foundations that make Claude enterprise-ready at scale
  2. Own engineering execution end-to-end: planning, prioritization, delivery quality, team health, and incident response
  3. Partner with engineering teams throughout the company to ensure that the platforms we build are extensible and easy to adopt
  4. Partner with sales and customer success on enterprise deals—understanding requirements, representing engineering in key conversations, and turning what you learn into priorities
  5. Shape the roadmap with product and design, not just execute against it

Skills

Required

  • 4+ years of experience as an engineering manager
  • experience in enterprise SaaS, cloud services, or admin tools
  • executing at a fast pace
  • detail-oriented and quality-focused
  • comfortable working with enterprise customers
  • skilled engineering manager who treats management as a craft

Nice to have

  • Experience with AI/ML products and understanding how enterprises evaluate and deploy AI tools
  • Background with compliance frameworks for regulated industries (SOC2, HIPAA) and enterprise audit logging requirements
  • Experience building integrations, permissions, billing, or pricing infrastructure
  • Familiarity with data residency and sovereignty requirements across global regions
  • Startup experience, particularly in scaling enterprise platforms from early adoption to broad deployment
  • Experience in working with research to improve domain specific model capabilities

What the JD emphasized

  • rigorous requirements around security, compliance, and control
  • enterprise-ready at scale
  • foundational systems that large organizations require
  • removing the deployment blockers that prevent large organizations from adopting Claude broadly
  • enterprise SaaS, cloud services, or admin tools
  • detail-oriented and quality-focused
  • working with enterprise customers
  • compliance frameworks for regulated industries (SOC2, HIPAA)
  • data residency and sovereignty requirements