Research Engineer - Distributed Training

About the Role

Prime Intellect is building the open superintelligence stack - from frontier agentic models to the infra that enables anyone to create, train, and deploy them. We aggregate and orchestrate global compute into a single control plane and pair it with the full rl post-training stack: environments, secure sandboxes, verifiable evals, and our async RL trainer. We enable researchers, startups and enterprises to run end-to-end reinforcement learning at frontier scale, adapting models to real tools, workflows, and deployment contexts.

As a Research Engineer working on Distributed Training, you'll play a crucial role in shaping our technological direction, focusing on our decentralizing AI training stack. If you love scaling things and maximizing training efficiency, this role is for you.

Responsibilities

• Lead and participate in novel research to build a massive scale, highly reliable and secure decentralized training orchestration solution.
• Optimize the performance, cost, and resource utilization of AI workloads by leveraging the most recent advances for compute & memory optimization techniques.
• Contribute to the development of our open-source libraries and frameworks for distributed model training.
• Publish research in top-tier AI conferences such as ICML & NeurIPS.
• Distill highly technical project outcomes in layman approachable technical blogs to our customers and developers.
• Stay up-to-date with the latest advancements in AI/ML infrastructure and tools, decentralized training research and proactively identify opportunities to enhance our platform's capabilities and user experience.

Requirements

• Strong background in AI/ML engineering, with extensive experience in designing and implementing end-to-end pipelines for training and deploying large-scale AI models.
• Deep expertise in distributed training techniques, frameworks (e.g., PyTorch Distributed, DeepSpeed, MosaicML’s LLM Foundry), and tools (e.g. Ray) for optimizing the performance and scalability of AI workloads.
• Experience in large-scale model training incl. distributed training techniques such as data, tensor & pipeline parallelism.
• Solid understanding of MLOps best practices, including model versioning, experiment tracking, and continuous integration/deployment (CI/CD) pipelines.
• Passion for advancing the state-of-the-art in decentralized AI model training and democratizing access to AI capabilities for researchers, developers, and businesses worldwide.