Anthropic
Security Engineer / Research Engineer, RL Security
$320K–$405K
San Francisco, CA +3 · Full-time · Posted Oct 20, 2026
- Location
- San Francisco, CA
New York City, NY
Seattle, WA
San Francisco, California, United States - Experience
- Years required correlate with internal job level; no fixed 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 available, but not for every role or candidate; the company will make every reasonable effort to obtain a visa if an offer is made.
About the job
Build and operate security controls, tooling, and observability for large-scale reinforcement-learning training systems and their infrastructure.
What you'll do
- Design, build, and operate security controls across RL training systems and infrastructure.
- Review and fix security issues across the RL stack; build shared libraries, tooling, and observability.
- Collaborate with researchers and performance engineers to preserve training correctness and performance, and document designs.
What they ask for
- Strong software engineering skills in Python and at least one systems language such as Rust, C++, or Go.
- Experience designing, building, and operating large-scale distributed systems or production security systems.
- Strong understanding of security fundamentals; working knowledge of Linux, containers, and cloud infrastructure.
- Experience debugging complex issues across many hosts and services; ability to reason about security, performance, and reliability tradeoffs.
Nice to have
- Experience running ML training or inference infrastructure at scale.
- Experience with infrastructure security, Kubernetes, sandboxed or virtualized execution, fleet observability, research teams, or RL/LLM workloads.
Pay
$320,000 — $405,000 USD (base salary)
Tools
Python, Rust, C++, Go, Linux, Kubernetes
AI focus
Securing large-scale reinforcement-learning training systems and the infrastructure supporting them.
Model-written summary · the original posting is authoritative