Applied Research - Forward-Deployed

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. 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. This is not a traditional research role. You'll spend most of your time embedded with customers, understanding their models, workflows, and goals. Then, you'll translate those objectives into concrete training runs, environment designs, evaluation harnesses, and deployment recipes using the Lab stack. You are the person who makes the platform work in practice for real workloads. You'll work closely with our research, product, and infrastructure teams to feed field insights back into the platform, shaping what we build next based on what customers actually need. 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 - 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 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