Research Engineer - Reinforcement Learning
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
Lead research on large-scale synthetic data generation and orchestration, optimize AI inference performance and resource utilization, develop open-source synthetic data and distributed reinforcement learning frameworks, publish research, and communicate technical outcomes through accessible technical writing.
Requirements
- Strong AI/ML engineering background with experience designing and implementing end-to-end pipelines for inference or training of large-scale AI models
- Deep expertise in distributed inference techniques and frameworks such as vLLM and SGLang
- Understanding of MLOps practices including model versioning, experiment tracking, and CI/CD pipelines
- Passion for advancing reasoning and democratizing access to AI capabilities
Responsibilities
- Lead and participate in novel research to build a large-scale synthetic data generation pipeline and orchestration solution
- Optimize the performance, cost, and resource utilization of AI inference workloads
- Contribute to open-source libraries and frameworks for synthetic data generation and distributed reinforcement learning
- Publish research in top-tier AI conferences such as ICML and NeurIPS
- Explain technical project outcomes through accessible technical blogs for customers and developers
- Track advances in AI/ML infrastructure, tools, and synthetic data research and identify platform improvements
Benefits
- Equity incentives
- Flexible work arrangements
- Remote or in-person work options
- Visa sponsorship
- Relocation assistance
- Quarterly team off-sites
- Hackathons
- Conferences
- Learning opportunities