RL Environments Engineer - Agent Evaluation Systems

MaxIT Consulting - Max Corporate Group; San Francisco (CA); On-site; USD 140,000 - 195,000; Full time

Job summary

MaxIT Consulting - Max Corporate Group in San Francisco is seeking a Research Engineer - RL Infrastructure & Agent Environments to build environments, evaluation systems, and supporting infrastructure used to train and assess long-horizon enterprise AI agents.

You will work on engineering and research problems behind realistic agent environments, post-training systems, and reliable evaluation of complex multi-step workflows. This role anchors in San Francisco with strong in-person collaboration.

Qualifications

• Hands-on experience with AI environments, evaluations, reinforcement learning infrastructure, or related agent-training systems.
• Strong software engineering fundamentals.
• Ability to build and ship technical infrastructure.
• Understanding of evaluation methodology, reward design, graders, and agent trajectories.

Responsibilities

• 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.

Skills

RL environments

Agent training systems

Software engineering

Evaluation methodology

Job description

MaxIT Consulting - Max Corporate Group in San Francisco is seeking a Research Engineer - RL Infrastructure & Agent Environments to build environments, evaluation systems, and supporting infrastructure used to train and assess long-horizon enterprise AI agents.

You will work on engineering and research problems behind realistic agent environments, post-training systems, and reliable evaluation of complex multi-step workflows. This role anchors in San Francisco with strong in-person collaboration.