Applied Research - Forward-Deployed
AI summary of the role
Forward-Deployed Research Engineer builds and runs custom RL training and eval setups on Lab for AI companies, labs, and enterprises.
What you’ll do
- Embed with strategic customers to understand agent architectures and design custom RL environments and eval harnesses.
- Configure and launch training runs on Lab, iterating on reward functions and rollout strategies.
- Codify repeatable customer patterns into reference implementations, templates, and documentation.
What you’ll bring
- Deep hands-on experience building, evaluating, or deploying LLM-based agents in past 1-2 years.
- Working understanding of RL and post-training concepts (GRPO, RLHF, reward modeling, SFT).
- Strong Python skills and comfort with modern AI stack (Hugging Face, inference engines, agent frameworks).
Technologies
Python · Hugging Face · RL · GRPO · RLHF · SFT · DSPy · LangGraph · MCP · React · TypeScript · Next.js
About Prime Intellect
Distributed infrastructure platform orchestrating global compute and enabling researchers, startups, and enterprises to train and deploy frontier agentic AI models at scale.
Series B
Source and classification
Implementation & delivery · Evidence for this classification:
$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.
More from the job description
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 inters [... source excerpt omitted ...] xist 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 cl [... source excerpt omitted ...] h 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 strate
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