Applied Research - RL & Agents
Prime Intellect builds the 'Open Superintelligence Stack' — an integrated compute, training, inference, and sandbox platform that lets companies train, deploy, and continuously improve their own AI models and agents. It serves AI startups, 'neolabs', and enterprises (over 6,000 customers, including Ramp and Zapier) that want to own their model optimization loop rather than rely solely on closed frontier labs.
Maintainer signals as of 8/14/2026
Funding history
Investors
Projects
About Prime Intellect
Prime Intellect is a San Francisco-based AI infrastructure company building what it calls the Open Superintelligence Stack: a full-stack platform spanning GPU compute (on-demand and reserved clusters), large-scale reinforcement learning training ('Lab'), an Environments Hub with 2,500+ community RL environments, hosted evaluations, sandboxed code execution, and dedicated/serverless model inference with native LoRA support. The company maintains open-source libraries (verifiers and prime-rl) used to build and train RL environments, and publishes frontier open research such as the INTELLECT and SYNTHETIC model/dataset series. Prime Intellect works with AI startups, enterprises, and 'neolab' customers such as Ramp and Zapier, helping them turn production traces and evaluations into custom-trained, post-trained agent models that outperform closed frontier models on specific workflows at lower cost and latency. The company has raised over $150M in total funding, including a $130M Series A led by Radical Ventures with participation from NVIDIA Ventures, Intel Capital, and Dell Technologies Capital, and reports over $100M in annualized revenue.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will design and deploy reinforcement-learning methods, post-training systems, evaluations, and AI agents for real-world workflows. You will build agent infrastructure, integrate frameworks, maintain distributed training and inference pipelines, and develop observability systems for reliable production deployments.
Requirements
- Strong machine learning engineering background
- Experience in post-training, reinforcement learning, or large-scale model alignment
- Experience with agent frameworks and tooling such as DSPy, LangGraph, MCP, and Stagehand
- Familiarity with distributed training and inference frameworks such as vLLM, sglang, Accelerate, Ray, and Torch
- Research contributions through publications, open-source contributions, or benchmarks in machine learning or reinforcement learning
- Technical writing abilities
- Research taste
- External collaboration and open-source community engagement
Responsibilities
- Design and iterate on AI agents for workflow automation, reasoning-intensive tasks, and large-scale decision-making
- Develop systems and frameworks for reliable and efficient agent operation
- Translate ambiguous objectives into technical requirements
- Deploy agents, evaluations, and harnesses for real-world tasks
- Shape verifiers, environments, training services, and research platform offerings
- Build reference implementations and recipes
- Design and implement reinforcement learning and post-training methods
- Build evaluations and harnesses for reasoning, robustness, and agentic behavior
- Prototype multi-agent and memory-augmented systems
- Integrate agent frameworks
- Architect and maintain distributed training and inference pipelines
- Develop observability and monitoring systems
Benefits
- Equity incentives
- Flexible work
- Visa sponsorship
- Relocation support
- Professional development budget
- Team off-sites
- Conference attendance