Research Engineer - Reinforcement Learning
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 8/12/2026
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 lead and participate in research on large-scale synthetic data generation and orchestration. You will optimize AI inference performance, cost, and resource utilization, develop open-source synthetic data and distributed reinforcement learning frameworks, publish research, and communicate technical outcomes through accessible technical writing.
Requirements
- AI/ML engineering experience
- End-to-end large-scale model inference or training pipelines
- Distributed inference
- vLLM
- SGLang
- MLOps
- Model versioning
- Experiment tracking
- CI/CD pipelines
Responsibilities
- Lead and participate in research on synthetic data generation
- Build a large-scale synthetic data generation pipeline and orchestration solution
- Optimize AI inference performance, cost, and resource utilization
- Develop open-source synthetic data generation libraries and frameworks
- Develop distributed reinforcement learning frameworks
- Publish research at top-tier AI conferences
- Explain technical project outcomes through accessible technical blogs
- Track advances in AI/ML infrastructure, tools, and synthetic data research
- Identify opportunities to improve platform capabilities and user experience
Benefits
- Equity incentives
- Flexible work arrangements
- Remote or in-person work options
- Visa sponsorship
- Relocation assistance
- Quarterly team off-sites
- Hackathons
- Conferences
- Learning opportunities