Software Engineer, People Products
Remote-Friendly (Travel Required) | San Francisco, CAFull-TimeMid-levelSoftware Engineering
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.
- 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
- Derive joy from hard work and the act of creation.
- Have shipped LLM-native features or applications.
- 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.
- Derive joy from hard work and the act of creation.
- Have shipped LLM-native features or applications.
- 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.
- 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.
How we're different
- We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
- The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
