RL Infrastructure Engineer: Agent Environments & Evaluations

MaxIT Consulting - Max Corporate Group; San Francisco (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

MaxIT Consulting - Max Corporate Group in San Francisco is seeking a Research Engineer to build RL agent environments, evaluation systems, and supporting infrastructure for training and evaluating long-horizon enterprise AI agents.

The role emphasizes hands-on engineering for production-grade environments, graders, and rollout tooling, with a strong preference for in-person collaboration in San Francisco. PhD not required; strong engineering track is valued.

Qualifications

• 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.

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

AI environments

RL infra

Software fundamentals

Infrastructure deployment

Cross-language

Job description

MaxIT Consulting - Max Corporate Group in San Francisco is seeking a Research Engineer to build RL agent environments, evaluation systems, and supporting infrastructure for training and evaluating long-horizon enterprise AI agents.

The role emphasizes hands-on engineering for production-grade environments, graders, and rollout tooling, with a strong preference for in-person collaboration in San Francisco. PhD not required; strong engineering track is valued.