Applied Research - Forward-Deployed

Applied Research - Forward-Deployed Be Your Own Lab 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 https://www.primeintellect.ai/blog/fundraise (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. 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 ... (truncated, view full listing at source)