Applied Research - RL & Agents
Prime Intellect provides an open superintelligence stack for training, evaluating, deploying, and continuously improving AI agents and models. Its platform combines RL environments, hosted training, inference, GPU compute, secure sandboxes, and open-source research tooling for researchers, startups, and enterprises.
Maintainer signals as of 8/23/2026
Funding history
Projects
About Prime Intellect, Inc.
Prime Intellect operates an integrated AI infrastructure platform spanning Lab, hosted reinforcement-learning training, evaluations, environments, inference, secure sandboxes, and on-demand or reserved GPU compute. It also develops open-source tools including Verifiers, prime-rl, and Prime Agent, supporting workflows from environment creation and model evaluation through post-training and production deployment. The company serves researchers, startups, enterprises, and teams building agentic AI systems, with customer examples including Ramp and Zapier.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Design and deploy reinforcement-learning methods, post-training systems, evaluations, and AI agents for real-world workflows. 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