Security Engineer​/Research Engineer, RL Security

# Security Engineer / Research Engineer, RL Security Anthropic  
· anthropic.com Category:
Security Engineering

Location:

San Francisco, CA | New York City, NY | Seattle, WA## Quality of life

What the posting states. Hover or tap a perk to see the line it came from.
* Equity* Sponsors visas
* 22 weeks parental leave
* Flexible PTO
* Retirement match
* Learning budget
* Home-office stipend

About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role Reinforcement learning is how Claude learns to reason, write code, and act autonomously over long horizons. At frontier scale, an RL run is a large distributed system: training, sampling, and environment execution run concurrently across a large fleet of accelerators and hosts. As an engineer in RL Engineering, you'll work on keeping that system secure as it scales.

This is a hands-on engineering role. You'll work in the RL codebase and the infrastructure underneath it, alongside the engineers and researchers who build and use it. The system is large and changes as fast as the research does, so security work here has to keep up with that pace without costing performance or velocity.

We're hiring from two backgrounds: security engineers who want to work on large-scale ML systems, and ML systems or research engineers who want to work on security. Strong candidates have depth in one and real interest in the other.

Key responsibilities

Design, build, and operate security controls for RL training systems and the infrastructure they run onWork across the stack (orchestration, environments, training, inference) wherever the work is needed

Review designs and code across the RL stack, find security problems, and fix them Build shared libraries and tooling that other RL engineers and researchers build on Build the observability needed to understand what these systems are doing

Work with researchers and performance engineers to make sure security changes preserve training correctness and performance

Write clear design documents for what you build

Minimum qualifications

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 security systems in production

Strong understanding of security fundamentals

Working knowledge of Linux, containers, and cloud infrastructure

Experience debugging complex problems across many hosts and services

Ability to reason about tradeoffs between security, performance, and reliability

Strong written communication, including design documents

Preferred qualifications

Experience running ML training or inference infrastructure at scale

Experience with infrastructure security, such as isolation, access control, networking, or identity

Experience with container orchestration such as Kubernetes, and with sandboxed or virtualized code execution at scale

Experience building observability for large fleets

Experience supporting research teams or other fast-moving internal users

Familiarity with reinforcement learning or large language model training workloads

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ( OTE ) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:$320,000 $ 405,000 USDLogistics

Minimum education:

Bachelor’s degree or an equivalent combination of education, training, and/or…