Applied Research - Forward-Deployed

About the role

We're looking for a Forward-Deployed Research Engineer (FDRE) to serve as the primary technical interface between Prime Intellect and our most important customers: AI companies, research labs, and enterprises running post-training and agentic RL on our platform.

What You'll Do

• Customer Engagement & Technical Delivery
• 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
• Arcitect 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
• Platform Feedback & Ecosystem
• 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
• Applied Research & Experimentation
• 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

What We're Looking For

• Deep hands-on experience building, evaluating, or deploying LLM-based agents in the past 1–2 years — you've seen what breaks in production and know what good evals look like
• Strong intuition for evaluation design: you can look at a customer's agent and quickly identify what to measure, how to construct a rubric, and where the reward signal is weak
• Working understanding of RL and post-training concepts (GRPO, RLHF, reward modeling, SFT) — you don't need to have written a trainer from scratch, but you should understand what the knobs do and why they matter
• 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 — you're comfortable in a room with a customer's engineering team figuring out what to build
• Excellent written and verbal communication — you can write a clear environment spec, a compelling case study, and a useful Slack message to a frustrated customer
• High agency and comfort with ambiguity. You don't wait for specs; you scope the problem, ship a solution, and iterate

Nice-to-Haves

• 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

Growth Opportunity

You’ll join a mission-driven team working at the frontier of open, superintelligence infra. In this role, you’ll have the opportunity to:

• Shape the evolution of agent-driven solutions—from research breakthroughs to production systems used by real customers.
• Collaborate with leading researchers, engineers, and partners pushing the boundaries of RL and post-training.
• Grow with a fast-moving organization where your contributions directly influence both the technical direction and the broader AI ecosystem.