Staff+ Software Engineer, Public Sector
Anthropic· Engineering & Design - Product
Hybrid (Remote-friendly US)$405k - $485kFull-timePosted 5mo ago
About the role
Remote-Friendly role for a Staff+ Software Engineer focused on public sector applications, working with Anthropic's Claude product in government and allied democracies. Emphasis on building, deploying, and scaling AI software in regulated environments, with cross-functional collaboration across research, safeguards, sales, legal, and policy teams.
Responsibilities
- Architect and ship deployments that meet public sector compliance and security requirements, from FedRAMP to classified networks
- Build AI applications tailored to government workflows by developing deep understanding of how agencies actually operate
- Partner with research and safeguards teams to evaluate and improve Claude for government use cases
- Work directly with government customers and internal go-to-market teams to turn requirements into technical and product roadmaps
- Design scalable systems for deployment, user management, and administrative controls across constrained environments
- Create clarity and technical direction in a fast-moving environment with unique constraints
Qualifications
- Experience building AI products and a working intuition for their capabilities and limitations
- A strong technical track record building and shipping enterprise- or government-grade products
- Excellent cross-functional collaboration skills — you’ll partner with sales, legal, research, and policy teams alike
Skills
Required:AI product developmentEnterprise- or government-grade softwareCross-functional collaboration
Benefits
- competitive compensation and benefits
- optional equity donation matching
- generous vacation and parental leave
- flexible working hours
- lovely office space for collaboration
Last updated Jul 8, 2026. Interested? Apply on the company site.
About Anthropic
building reliable, interpretable, and steerable AI systems
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
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