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Applied Research - RL & Agents

Prime Intellect

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Location:
United States , San Francisco

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Category:
IT - Software Development

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Not provided

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Salary:

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Job Description:

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.

Job Responsibility:

  • 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 abilities (documentation, blogs, papers) and research taste
  • Eagerness to drive collaborations with external partners and engage with the broader open-source community

Nice to have:

  • Experience with web programming (React, TypeScript, Next.js)
  • Experience running LLM evaluations and/or synthetic data generation
  • Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform)
What we offer:
  • Competitive Compensation + equity incentives
  • Flexible Work (San Francisco or hybrid-remote)
  • Visa Sponsorship & relocation support
  • Professional Development budget
  • Team Off-sites & conference attendance

Additional Information:

Job Posted:
February 21, 2026

Employment Type:
Fulltime
Work Type:
On-site work
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