Research Engineer - Data and Evaluation

The role

You will build tasks, environments, and evaluations to train models through RL. You will work with researchers to investigate model failures and develop scoring logic.

What you will do

• Design and build challenging evaluations for coding and long-horizon agents.
• Build executable environments in Verifiers, with realistic task setup and clear success criteria.
• Develop and test scoring logic and verifiers, investigating reward hacks and misleading measures of progress.
• Trace failures through model outputs and harness code to distinguish model limitations from problems in the environment or scoring.
• Build and scale RL data pipelines that generate and curate challenging coding and agentic tasks.
• Work with RL researchers to test whether changes to tasks and training data improve performance on held-out tasks.

Required qualifications

• Strong software engineering skills in Python and experience building data pipelines, evaluation systems, or ML workflows used by others.
• Hands-on experience working with language models or agents, with an understanding of how training data and evaluation choices affect model behavior.
• Ability to inspect complex data and model outputs, identify failure modes, and turn an ambiguous quality question into a testable measurement.
• A record of owning technical projects through experimentation, implementation, and validation, while collaborating closely with researchers and engineers.
• Hands-on experience building technical evaluations or agent environments, with the engineering depth to inspect model traces and debug harness and scoring code.

Nice to have

• Large-scale synthetic data generation, dataset curation, or distributed processing for model training.
• Evaluation methodology, executable coding tasks, agent environments, verifiers, or reward design.
• Experiment analysis, statistical testing, contamination detection, and reproducible research tooling.
• Experience with RL or post-training data, including task difficulty selection and analysis of rollout or reward distributions.
• Contributions to Verifiers, Prime-RL, or comparable environment and evaluation frameworks.
• Work on model-based grading, long-horizon coding agents, or diagnosing reward exploitation.

Benefits

• Cash Compensation Range of $150-350k, including equity incentives
• Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.
• Visa sponsorship and relocation assistance for international candidates.
• Quarterly team off-sites, hackathons, conferences and learning opportunities.
• Opportunity to work with a talented, hard-working and mission-driven team

Before you apply

• Visa sponsorship and relocation assistance offered for international candidates.

About the company

Prime Intellect is building the open superintelligence stack. The platform Lab unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system.