MaxIT Consulting - Max Corporate Group
Evals Research Engineer – LLM Agents & RL Environments
San Francisco, California, US
Role snapshot
MaxIT Consulting - Max Corporate Group is hiring a Evals Research Engineer – LLM Agents & RL Environments on a full-time basis. This is an on-site role based in San Francisco, California, US.
- Company
- MaxIT Consulting - Max Corporate Group
- Location
- San Francisco, California, US
- Work mode
- On-site
- Job type
- Full-time
- Posted
- Nov 3, 2026
Who this role suits
The listing does not state a required experience level here. Read the requirements in the description below and apply if they match your background.
How to prepare your application
- Match your résumé to the requirements in the description, using the same wording where it is honest to do so.
- Prepare a short example for each requirement you meet, and be ready to explain any you only partly meet.
- Read what MaxIT Consulting - Max Corporate Group publishes about its product or team, so you can say clearly why this role and this company.
Apply only on MaxIT Consulting - Max Corporate Group's own site. A genuine employer never asks candidates for money, deposits or bank details to apply, interview or “secure” a job. If something looks wrong, use Report a problem with this listing and we will review it. Read how we check listings.
About the role
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 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.