Anthropic
Software Engineer, Research Productivity
$405K–$625K
San Francisco, CA +2 · Full-time · Posted Jul 6, 2026
- Location
- San Francisco, CA
New York City, NY
San Francisco, California, United States - Experience
- Years required correlate with the internal job level requirements; no specific number stated.
- Education
- Bachelor’s degree or an equivalent combination of education, training, and/or experience; relevant field demonstrated through coursework, training, or professional experience.
- Visa sponsorship
- Visa sponsorship is offered, but may not be available for every role or candidate; Anthropic will make every reasonable effort to obtain a visa after an offer.
About the job
Builds and scales infrastructure and tools that accelerate research workflows across Anthropic.
What you'll do
- Design, build, and operate reliable, scalable research infrastructure as usage and requirements evolve.
- Independently lead complex, multi-month projects and drive technical alignment across teams.
- Partner with researchers to understand workflows, make architectural decisions, and help set team standards and mentor engineers.
What they ask for
- Experience designing, building, and operating large-scale distributed systems or production infrastructure.
- Track record of independently scoping and delivering complex, ambiguous, multi-month technical projects.
- Strong software engineering fundamentals and hands-on coding ability; experience making architectural decisions others build on.
- Strong communication skills and experience driving alignment across teams or stakeholders; ability to work effectively in ambiguous, fast-changing environments.
Nice to have
- Experience building infrastructure or platforms for research or machine-learning workflows; familiarity with large-scale ML research needs.
- Experience with distributed systems, cloud infrastructure, and infrastructure-as-code; technical leadership or mentoring experience.
Pay
$405,000 — $625,000 USD (base salary)
Tools
distributed systems, cloud infrastructure, infrastructure-as-code
AI focus
Build infrastructure and tools supporting research workflows, including familiarity with compute and tooling needs of large-scale machine-learning research.
Model-written summary · the original posting is authoritative