Staff Software Engineer, People Products

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role In case you hadn’t noticed, Anthropic is growing fast. Really, really fast. The People Products team exists to support Anthropic’s mission by defining the blueprint for AI at work. We help hire the best person in the world for every job, ensure manager effectiveness, ramp new hires successfully, and ensure that we apply first principles thinking in how we shape Anthropic’s culture through the tools we build. We cover the entire employee lifecycle from hiring to onboarding, teamwork, and promotions. You’ll work directly with Claude, with access to capabilities no external team has, on problems that are genuinely unsolved. You’ll move fast — prototype to production in days or weeks. We believe in cross-functional thinkers who can reason across product, design, and engineering. You’ll be given high autonomy, own your decisions, and ship constantly. If you’ve experienced the pain of bad people practices and want to be the person who fixes them at the most consequential AI company in the world, this is that job. Responsibilities - Build full-stack end-to-end across the People Products portfolio. - Design and implement AI-native workflows: build tools, evals, prompts, and products. You’ll help define what is possible in applied AI for people processes. - 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. No gatekeeping, you are expected to talk to your customers. - Make product and architecture decisions independently in a low-structure environment: knowing when to cut scope, when to ship, and when to ask for input. - Contribute ideas for how the team works, what it builds, and where applied AI can have the most leverage in people workflows. You Might Be a Good Fit If You: - Have 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 - Have shipped LLM-native features or applications. - Derive joy from hard work and the act of creation. - Are experienced enough to build big features independently, and make great architectural decisions along the way. - Are 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. - Are genuinely mission-driven: you care about the intersection of AI and people practices, not just the technical puzzle. - Are a collaborative, supportive teammate: you bring people along, communicate clearly about tradeoffs, and make the people around you better. Strong Candidates May Also Have: - 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. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000—$405,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that peopl

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. Ability to build big features independently and make great architectural decisions. Self-sufficient end-to-end: able to go from idea to production without needing a designer, PM, or architect to unblock you. Comfortable talking to internal customers, hearing hard feedback, and incorporating it quickly. Genuine mission-driven interest in the intersection of AI and people practices. Collaborative and supportive teammate who communicates tradeoffs clearly. Familiarity with MCP (Model Context Protocol) or prior experience building Claude or LLM integrations in production (strong plus). Experience with HR tech platforms such as Greenhouse, Workday, or Rippling (strong plus).

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. No gatekeeping, you are expected to talk to your customers. Make product and architecture decisions independently in a low-structure environment: knowing when to cut scope, when to ship, and when to ask for input. Contribute ideas for how the team works, what it builds, and where applied AI can have the most leverage in people workflows.