Member of Technical Staff – RL Environments & Evals

MaxIT Consulting - Max Corporate Group; San Francisco (CA); On-site; USD 170,000 - 250,000; Full time

Job summary

MaxIT Consulting - Max Corporate Group in San Francisco seeks a Research Engineer to build agent environments, evaluation systems, and infrastructure used to train and assess long-horizon enterprise AI agents.

You will design evaluation environments, define tasks and rewards, and develop production-grade infrastructure for rollouts and trajectory inspection, focusing on reliability and scalability in real-world enterprise settings.

Qualifications

• Hands-on experience with AI environments, evaluations, or RL infrastructure.
• Strong software engineering fundamentals.
• Ability to build and ship technical infrastructure.
• Understanding of evaluation methodology and reward design.
• Ability to work across languages and stacks.

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 infrastructure

Software engineering fundamentals

Technical infrastructure

Reward design

Graders

Agent trajectories

Polyglot programming

Job description

San Francisco, California | Primarily On-site

We are seeking an Research Engineer – RL Infrastructure & Agent Environments to 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 engines, 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.