Job Description

Job Description

Job Description

San Francisco, California | Primarily On-site

We are seeking an AI Evaluation Engineer – Reinforcement Learning & Agents 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.

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