Staff Software Engineer, People Products at Anthropic

Remote - San Francisco, CA, USA

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Anthropic is seeking a Staff Software Engineer for the People Products team, responsible for building and shipping AI-native tools that support the entire employee lifecycle. The role requires deep full‑stack engineering expertise, experience with LLM‑powered features, and the ability to work autonomously with internal stakeholders to deliver high‑quality solutions in a fast‑paced environment.

Salary

USD 320,000 - 405,000

Requirements

Skills

  • 8+ years of relevant experience as a Fullstack or product engineer, with a track record of leading complex, multi-month projects or teams as a tech lead or equivalent
  • Shipped LLM-native features or applications
  • Derive joy from hard work and the act of creation
  • Experienced enough to build big features independently, and make great architectural decisions along the way
  • Self-sufficient end-to-end: you can go from idea to production without needing a designer, PM, or architect to unblock you
  • Move fast without cutting corners: you hold a high quality bar and know how to make smart tradeoffs under time pressure
  • Engage directly with users and criticism: you’re comfortable talking to internal customers, hearing hard feedback, and incorporating it quickly
  • Genuinely mission-driven: you care about the intersection of AI and people practices, not just the technical puzzle
  • Collaborative, supportive teammate: you bring people along, communicate clearly about tradeoffs, and make the people around you better
  • Familiarity with MCP (Model Context Protocol) or prior experience building Claude or LLM integrations in production
  • Background at an AI-native company or in a product-focused 0‑>1 engineering environment
  • Experience with HR tech platforms such as Greenhouse, Workday, or Rippling

Responsibilities

  • Build full-stack end-to-end across the People Products portfolio
  • Design and implement AI-native workflows: build tools, evals, prompts, and products
  • Work directly with internal stakeholders—HR teams, recruiters, managers—to understand problems, gather feedback, and iterate quickly without waiting for requirements to be handed down
  • Make product and architecture decisions independently in a low-structure environment
  • Contribute ideas for how the team works, what it builds, and where applied AI can have the most leverage in people workflows

Technologies

ClaudeLLMMCP (Model Context Protocol)GreenhouseWorkdayRippling

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