RL Environments Engineer

MaxIT Consulting - Max Corporate Group; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

MaxIT Consulting - Max Corporate Group in San Francisco is 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 evaluation, and reliable assessment of complex multi-step workflows.

Qualifications

• Hands-on experience with AI environments, evaluations, and reinforcement learning infrastructure.

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

Reinforcement learning

Evaluation systems

Infrastructure development

Job description

San Francisco, California | Primarily On-site

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

The Opportunity

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

Key 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.

Required 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.

Candidate Profile

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

Seniority

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.

Work Arrangement

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.