Remote Technical Reviewer - RL Environment Terminal Benchmarking (Agentic AI) - AI Trainer ($25-$30 per hour)

Mercor is hiring a Technical Reviewer on behalf of a leading AI lab to evaluate and refine benchmarking pipelines for reinforcement learning (RL) environments and agentic AI systems. In this role, you’ll be responsible for

• reviewing environment design, terminal conditions, and evaluation protocols
• to ensure accuracy, reproducibility, and fairness in benchmarking. You’ll work closely with researchers and engineers to provide technical feedback that strengthens experimental rigor and system reliability.
• ###
• You’re a great fit if you :
• Have a background in
• reinforcement learning, computer science, or applied AI research
• . - Are experienced with
• RL environments
• . - Understand
• benchmarking methodologies, terminal conditions, and evaluation metrics
• for RL tasks. - Are comfortable reading and reviewing codebases in
• Python
• (PyTorch / TensorFlow a plus). - Have strong critical thinking skills and can provide
• structured technical feedback
• . - Care deeply about
• experimental reproducibility, fairness, and standardization
• in agentic AI. - Are detail-oriented and capable of reviewing both
• theoretical formulations and implementation details
• ###
• Primary Goal of This Role
• To review, validate, and improve reinforcement learning environment benchmarking pipelines, ensuring that terminal conditions, evaluation metrics, and system behaviors are robust, reproducible, and aligned with agentic AI research goals.
• ###
• What You’ll Do
• Review RL environments and
• evaluate terminal conditions
• for correctness and consistency. - Assess
• benchmarking pipelines
• for fairness, reproducibility, and alignment with research objectives. - Provide
• structured technical feedback
• on code implementations and documentation. - Collaborate with researchers to refine
• evaluation metrics and methodologies
• . - Ensure reproducibility by validating results across different
• runs, seeds, and hardware setups
• . - Document findings and recommend improvements for
• environment design and benchmarking standards
• ###
• Why This Role Is Exciting
• You’ll directly influence the
• reliability of benchmarking in agentic AI research
• . - You’ll work on
• cutting-edge RL environments
• that test the limits of intelligent agents. - You’ll help establish
• standards for evaluation and reproducibility
• in a fast-moving field. - You’ll collaborate with researchers shaping the
• future of agentic AI systems
• ###
• Pay & Work Structure
• You’ll be classified as a
• full-time hourly contractor
• to Mercor. - Paid weekly via Stripe Connect, based on hours logged. - 40 hours / week commitment with flexible scheduling. - Remote and flexible working style.