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Applied Research - Forward-Deployed

United States, San Francisco 150000.00 - 300000.00 USD / Year · Job Posted May 27, 2026
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Job Description

Prime Intellect builds the infrastructure that frontier AI labs build internally, and makes it available to everyone. Our platform, Lab, unifies environments, evaluations, sandboxes, and high-performance training into a single full-stack system for post-training at frontier scale, from RL and SFT to tool use, agent workflows, and deployment. We validate everything by using it ourselves, training open state-of-the-art models on the same stack we put in your hands. We're looking for people who want to build at the intersection of frontier research and real infrastructure. We recently raised $15mm in funding (total of $20mm raised) led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy (Eureka AI, Tesla, OpenAI), Tri Dao (Chief Scientific Officer of Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Huggingface), Emad Mostaque (Stability AI) and many others.

Job Responsibility

  • Embed directly with strategic customers to understand their agent architectures, failure modes, and product goals
  • Design and build custom RL environments, evaluation harnesses, and verifiers that capture what 'good' looks like for each customer's domain
  • Architect agent scaffolding — tool use, multi-step reasoning, memory, sandbox execution — tailored to customer workflows
  • Configure and launch training runs on Lab, iterating on reward functions, rollout strategies, and evaluation criteria
  • Serve as the technical lead for engagements end-to-end: from discovery through deployed, improved models
  • Identify repeatable patterns from customer engagements and codify them into reference implementations, templates, and documentation
  • Serve as the voice of the customer internally, shaping the roadmap for Lab, verifiers, the Environments Hub, and training infrastructure
  • Build high-quality examples and 'recipes' that make it easy for new customers and open-source contributors to extend the stack
  • Contribute to technical content (blog posts, tutorials, case studies) that demonstrates real-world platform usage
  • Develop novel evaluation methodologies for agentic behavior — multi-step reasoning, tool use correctness, recovery from failure, long-horizon task completion
  • Prototype and iterate on agent harnesses for real-world tasks: code generation, workflow automation, document processing, and more
  • Experiment with reward design, rubric construction, and environment shaping to improve training signal quality
  • Stay current on the frontier of agentic AI, evals, and post-training methods, and bring that knowledge directly into customer work

Requirements

  • Deep hands-on experience building, evaluating, or deploying LLM-based agents in the past 1–2 years
  • Strong intuition for evaluation design
  • Working understanding of RL and post-training concepts (GRPO, RLHF, reward modeling, SFT)
  • Strong Python skills and comfort with the modern AI stack (Hugging Face, inference engines, agent frameworks)
  • Experience in a customer-facing or consulting-adjacent technical role, or as a technical founder
  • Excellent written and verbal communication
  • High agency and comfort with ambiguity

Nice to have

  • Experience with agent frameworks and tooling (DSPy, LangGraph, MCP, Stagehand, browser automation)
  • Experience building or running LLM evaluation pipelines at scale (benchmarks, synthetic data generation, model grading)
  • Research experience — publications, open-source contributions, or benchmarks in ML/RL/agents
  • Familiarity with sandbox/code execution environments for agent evaluation
  • Web programming experience (React, TypeScript, Next.js) for building demos and customer-facing tooling

What we offer

  • Cash Compensation Range of $150-300k + equity incentives
  • Flexible Work (San Francisco or hybrid-remote)
  • Visa Sponsorship & relocation support
  • Professional Development budget
  • Team Off-sites & conference attendance

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