RL Environments Engineer

San Francisco California Primarily On-site

We are seeking an Agent Evaluation Infrastructure Engineer to build the environments evaluation systems and supporting infrastructure used to train and assess long-horizon enterprise AI agents.

You will work on the engineering and research problems behind realistic agent environments post-training systems and reliable evaluation of complex multi-step workflows.

• Design evaluation environments for long-horizon enterprise agent workflows.
• Define tasks state tools graders and reward signals used to evaluate and improve agents.
• Build high-fidelity representations of complex enterprise software environments.
• Develop infrastructure for rollouts orchestration trajectory inspection and grader pipelines.
• Measure both correctness and efficiency across multi-step agent behavior.
• Investigate evaluation failures reward-quality issues and agent behavior.
• Build production-quality systems rather than notebook-only research prototypes.

• Hands-on experience with AI environments evaluations reinforcement learning infrastructure or related agent-training systems.
• Strong software engineering fundamentals.
• Demonstrated ability to build and ship technical infrastructure.
• Understanding of evaluation methodology reward design graders and agent trajectories.
• Ability to work across languages and technology stacks based on system requirements.

A PhD is not required. Strong engineering and shipped environment or evaluation systems are more important than academic credentials or publication history.

The opportunity is open to exceptional new graduates early-career engineers and experienced senior candidates. Selection is based primarily on engineering strength and relevant technical work.

The role is anchored in San Francisco with a strong preference for in-person collaboration. Limited flexibility may be considered case by case for exceptional candidates.