Security Engineer / Research Engineer, RL Security

Anthropic · San Francisco, CA | New York City, NY | Seattle, WA · AI Research & Engineering · listed October 20, 2026

The shape of it

Seniority
Not stated
Where
Hybrid
Stated pay
$320,000 – $405,000 USD
Requirements listed
7
Length
1,237 words

In the posting’s own words

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.

What it asks for · 7

  • 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

Also a plus

  • 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

What the job covers

  • 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

Tools and skills named

Security & compliance
  • Security17×
Models & research
  • Inference4×
  • Machine learning4×
  • Reinforcement learning3×
  • LLM2×
Cloud & infra
  • Observability4×
  • Distributed systems2×
  • Kubernetes2×
  • Linux2×
Languages
  • C++2×
  • Go2×
  • Python2×
  • Rust2×

Words the posting leans on

  • security17×
  • systems15×
  • experience14×
  • build11×
  • training11×
  • infrastructure9×
  • design8×
  • engineers8×
  • large7×
  • performance7×
  • code5×
  • researchers5×
  • building4×
  • container4×
  • control4×
  • design documents4×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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How this page was made

An automated read of a public job posting, fetched November 2, 2026 and last changed by Anthropic on October 20, 2026. Every list above is pulled from the posting’s own sentences — nothing rewritten, nothing added, no judgment about the role or the company. Counts and seniority are read off the text by rule, so they can be wrong where the posting is unusual. The original is the only thing that binds. Openings close without warning; check the source before spending an evening on it.