Security Engineer / Research Engineer, RL Security
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 on
Work 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 USD