Research Engineer - Data and Evaluation

Prime Intellect • San Francisco, CA

2d • $150,000 - $350,000

About The Position

Prime Intellect is building an open superintelligence stack and a full-stack platform for post-training frontier models. The Research Engineer – Data and Evaluation will build tasks and environments used to train Prime Intellect’s models through reinforcement learning, as well as evaluations that measure their capabilities. The role works directly with researchers to investigate model failures, examining both task implementation and how success is scored. The position welcomes depth in data generation, environments and rewards, or evaluation systems; experience across every area is not required.

Requirements

• 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.
• Relevant experience may include large-scale synthetic data generation, dataset curation, or distributed processing for model training.
• Relevant experience may include evaluation methodology, executable coding tasks, agent environments, verifiers, or reward design.
• Relevant experience may include experiment analysis, statistical testing, contamination detection, and reproducible research tooling.

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

• Cash compensation range of $150,000–$350,000, including equity incentives.
• Flexible work arrangements, with the option to work remotely or in person at 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.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed