SF RL Agent Evaluation Infrastructure Engineer

MaxIT Consulting - Max Corporate Group; San Francisco (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

MaxIT Consulting - Max Corporate Group in San Francisco, CA is seeking an Agent Evaluation Infrastructure Engineer to build environments, evaluation systems, and the 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.

Qualifications

• Hands-on experience with AI environments, evaluations, or RL infrastructure.
• Strong software engineering fundamentals and production-quality infrastructure.
• Understanding of evaluation metrics, reward design, graders, and trajectories.
• Ability to work across languages and stacks as required by the system.

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 Infrastructure

Software Engineering

Infra Development

Evaluation Methodology

Cross-language Skills

Job description

MaxIT Consulting - Max Corporate Group in San Francisco, CA is seeking an Agent Evaluation Infrastructure Engineer to build environments, evaluation systems, and the 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.