Used Tools & Technologies
Machine LearningRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Security @ 4
Communication @ 4
AI @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. This role is part of the Public Sector engineering team, building specialized AI applications and products for government customers and scaling Anthropic’s products from 0 to 1 and beyond. Engineers in this role will work across the stack, own projects end-to-end, and collaborate with research and customers to adapt Claude for government workflows.
Responsibilities
- Build specialized AI applications and products for governments by developing deep understanding of their processes and workflows
- Take ownership of architecting new deployments and designing public sector specific features
- Collaborate closely with research on AI models for government applications and workflows
- Partner directly with government customers and internal go-to-market teams to translate requirements into technical and product roadmaps
- Design and build scalable systems for deployment, user management, and administrative controls
- Create clarity and technical direction in a fast-moving environment with unique constraints
Requirements
- 8+ years of experience as a full stack software engineer
- Experience integrating and working with AI/ML models and understanding their capabilities
- Strong technical background with proven success building and shipping enterprise or government-grade products
- Excellent collaboration skills and ability to work effectively across functions
- Minimum education: Bachelor’s degree or equivalent combination of education, training, and/or experience
- This position requires verification of U.S. citizenship due to citizenship-based legal restrictions for supporting U.S. federal, state, and/or local government customers
Strong Candidates May Also Have
- Startup experience, particularly in scaling products from zero to one
- Experience partnering with sales, customer success, and professional services teams to drive product adoption
- Experience delivering software to government networks
- Active federal security clearance (Secret or above)
Logistics & Compensation
- Locations: San Francisco, CA and New York City, NY (United States)
- Location-based hybrid policy: currently expect all staff to be in one of our offices at least 25% of the time
- Minimum years of experience: will correlate with internal job level requirements for the position
- Visa sponsorship: Anthropic states they do sponsor visas and retain an immigration lawyer to help, though sponsorship is not guaranteed for every role/candidate
- Annual Salary: $1 - $2 USD
Benefits
- Competitive compensation and benefits
- Optional equity donation matching
- Generous vacation and parental leave
- Flexible working hours and office space for collaboration
How We're Different
Anthropic focuses on large-scale, collaborative AI research and values communication, impact, and interdisciplinary approaches. The team works on a few large-scale research efforts and hosts frequent research discussions to pursue high-impact directions in AI safety and capability research.