Own Your Intelligence

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.

Role Impact

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.

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.

Requirements

• Strong software engineering skills in Python and experience building data pipelines, evaluation systems, or ML workflows used by others.