About the Role
Join an expert network supporting AI human-data projects focused on agents and reinforcement learning. You will design training environments and evaluation systems, and assess agent behavior as projects become available.
What You'll Do
Design reinforcement learning environments, including Python and Docker setups, state spaces, tool integrations, and scoring mechanisms.
Build robust verifiers and reward functions that resist agent gaming.
Translate real-world business workflows in areas such as ERP, CRM, finance, and operations into measurable scenarios.
Integrate tools and REST APIs, and build test harnesses using pytest or similar tools.
Lead agent demonstration and computer-use programs, setting standards for trace collection quality.
Grade and repair agent trajectories, and mentor contributors on evaluation practices.
What We're Looking For
At least 5 years of experience in software engineering, QA automation, machine learning, reinforcement learning, or business systems and operations paired with hands-on agent or RL data work.
Strong Python skills and experience building tools or services used by others.
Experience designing environments, verifiers, reward functions, or test harnesses that evaluate software or agent behavior, using tools such as pytest, Playwright, Selenium, or Gymnasium-style environments.
Hands-on experience with agent or RL data, such as demonstrations, trajectory grading, trace collection, or computer-use demos.
Experience with REST APIs and Docker.
Experience leading or reviewing data, evaluation, or demonstration work is valuable, as is expertise modeling workflows in business systems such as Salesforce or ERP platforms.
A degree in computer science, engineering, or a related field is relevant.
Compensation & Benefits
Compensation is $80 to $200 per hour, depending on project, field, and level. Pay is based on hours worked.
Location
Remote. Projects typically run from one week to several months.