Anthropic

Staff Software Engineer, People Products

Anthropic
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14 days ago
Remote, United States or San Francisco, CA, USAStaff+
H1B Sponsor

Base Salary

$320k - $405k/yr

Responsibilities

  • Build full-stack, end-to-end products across the People Products portfolio.
  • Design and implement AI-native workflows, including tools, evaluations, prompts, and products.
  • Work directly with HR teams, recruiters, managers, and other internal stakeholders to understand problems and iterate on solutions.
  • Make independent product and architecture decisions, including scope and delivery tradeoffs.
  • Contribute to the team’s product direction, working practices, and use of applied AI in people workflows.

Requirements

  • 8+ years of relevant experience as a full-stack or product engineer, including leading complex multi-month projects or teams as a tech lead or equivalent.
  • Experience shipping LLM-native features or applications.
  • Ability to independently take large features from idea to production and make sound architectural decisions.
  • Ability to work quickly while maintaining a high quality bar and making effective tradeoffs.
  • Comfort engaging directly with users and incorporating feedback.
  • Strong collaboration, communication, and mission alignment.

Benefits

  • Annual base salary range of $320,000-$405,000 USD.
  • Hybrid work policy requiring staff to be in an Anthropic office at least 25% of the time, with some roles requiring more office time.
  • Visa sponsorship is available for eligible roles and candidates, with immigration-lawyer support.
  • Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
Anthropic

About Anthropic

501-1,000 employees

We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.

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