Applied Research - RL & Agents

Applied Research - RL & Agents 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. ROLE IMPACT This is a role at the intersection of cutting-edge RL/post-training methods and applied agent systems. You’ll have a direct impact on shaping how advanced models are aligned, deployed, and used in the real world by: - Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale. - Building Robust Infrastructure: Developing the systems and frameworks that enable these agents to operate reliably, efficiently, and at massive scale. - Bridge Between Applications & Research: Translate ambiguous objectives into clear technical requirements that guide product and research priorities. - Prototype in the Field: Rapidly design and deploy agents, evals, and harnesses for real-world tasks to validate solutions. Application-Driven Research & Infrastructure - Shape the direction and feature set for verifiers, the Environments Hub, training services, and other research platform offerings. - Build high‑quality examples, reference implementations, and “recipes” that make it easy for others to extend the stack. - Prototype agents and eval harnesses tailored to real-world use cases and external systems. - Pair with technical end‑users (research teams, infra‑heavy customers, open‑source contributors) to design environments, evals, and verifiers that reflect real workloads. Post-training & Reinforcement Learning - Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks. - Build evaluations and harnesses and to measure reasoning, robustness, and agentic behavior in real-world workflows. - Prototype multi-agent and memory-augmented systems to expand capabilities for downstream applications. - Experiment with post-training recipes to optimize downstream performance. Agent Development & Infrastructure - Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making. - Extend and integrate with agent frameworks to support evolving feature requests and performance requirements. - Architect and maintain distributed training/inference pipelines, ensuring scalability and cost efficiency. - Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments. REQUIREMENTS - Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment. - Experience with agent frameworks and tooling (e.g. DSPy, LangGraph, MCP, Stagehand). - Familiarity with distributed training/inference frameworks (e.g., vLLM, sglang, Accelerate, Ray, Torch). - Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL. - Passion for advancing the state-of-the-art in reasoning and building practical, agentic AI systems. - Strong technical writing abiliti ... (truncated, view full listing at source)