Coding RL Engineer

About the role

An early-stage AI company is hiring a Coding RL Engineer to join its reinforcement learning team. The engineer builds coding environments and tasks used to train and evaluate advanced models, turning real-world software engineering work into reproducible benchmarks that help improve coding AI.

Key responsibilities

• Convert licensed production codebases into reproducible, containerized coding environments.
• Use issues, pull requests, commit histories and incident records to create tasks covering bug fixes, features, refactors, testing and multi-file agent work.
• Build and improve verifiers with hidden tests, rubric-based or model-based grading, and safeguards against reward hacking and answer leakage.
• Run model rollouts, review traces and repair or remove broken, ambiguous, too-easy or exploitable tasks.
• Contribute tasks and analysis to coding benchmarks and build tools that speed up task creation and validation.
• Work with research teams to calibrate task difficulty and benchmark coverage.

Required profile

• At least 2 years of professional software engineering experience delivering software systems.
• Experience navigating unfamiliar, complex codebases and getting them building, running and tested.
• Experience creating coding tasks from real engineering work and hardening verifiers against flaky tests, unclear requirements and leaked solutions.
• Relevant experience with frontier AI labs, evaluation teams, RL environments, coding agents or large enterprise and legacy codebases is helpful.

Required skills

• Very strong Python skills and fluency in another language such as TypeScript, Go, Java or Rust.
• Hands-on Docker experience and familiarity with reproducible build and test environments.
• Working knowledge of coding model training and evaluation, including SWE-bench-style tasks, code evaluations, RLVR and agent harnesses.

Location: remote within the United States or in person in San Francisco, California; full-time position.