320K–405K USD / year
Security Engineer / Research Engineer, RL Security
AIHybrid — San Francisco, CA, New York City, NY, Seattle, WA
Published on 2026-10-20
These details were extracted automatically from the original listing. They may not be complete or fully up to date — it's worth checking the original job post.
About this role
Build and operate security controls for Anthropic’s large-scale reinforcement learning training systems and supporting infrastructure. Work across the RL stack with engineers and researchers to improve security while preserving training performance and velocity.
About the company
Anthropic builds reliable and interpretable AI systems, including the Claude family of models, for businesses and developers. The company focuses on AI safety research and practical AI assistants for enterprise and consumer use.
The team
RL Engineering
Stack
PythonRustC++GoLinuxContainersCloud infrastructureKubernetes
What you'll do
- Design, build, and operate security controls for RL training systems and infrastructure
- Review and fix security issues across orchestration, environments, training, and inference
- Build shared libraries, tooling, and fleet observability
- Collaborate with researchers and performance engineers to preserve correctness and performance
- Write design documents
What we're looking for
- Strong Python skills and experience with at least one of Rust, C++, or Go
- Experience 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 hosts and services
- Ability to balance security, performance, and reliability
- Strong written communication
Nice to have
- Experience running ML training or inference infrastructure at scale
- Infrastructure security experience, including isolation, access control, networking, or identity
- Experience with Kubernetes and sandboxed or virtualized code execution at scale
- Experience building observability for large fleets
- Experience supporting research teams or fast-moving internal users
- Familiarity with reinforcement learning or large language model training workloads
Benefits
- Generous vacation and parental leave
- Flexible working hours
- Optional equity donation matching