Research Engineer - Data and Evaluation

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.

Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators, including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.

Build the tasks and environments that train Prime Intellect’s models through RL, and the evaluations that show what they can actually do. You’ll work directly with researchers to investigate model failures, from how a task is implemented to how success is scored.

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  Design and build challenging evaluations for coding and long-horizon agents.

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  Build executable environments in Verifiers, with realistic task setup and clear success criteria.

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  Develop and test scoring logic and verifiers, investigating reward hacks and misleading measures of progress.

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  Trace failures through model outputs and harness code to distinguish model limitations from problems in the environment or scoring.

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  Build and scale RL data pipelines that generate and curate challenging coding and agentic tasks.

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  Work with RL researchers to test whether changes to tasks and training data improve performance on held-out tasks.

• 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.

• 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.

We welcome depth in data generation, environments and rewards, or evaluation systems. You don’t need to have worked across every area.