Building Open Superintelligence Infrastructure
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.
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.
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.
If you're not familiar with these, but feel like that you can contribute to our mission and you're a high-energy person, get familiar with these resources (here, here and here) and please reach out!
Competitive compensation, including equity incentives, aligning your success with the growth and impact of Prime Intellect.
Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.
Visa sponsorship and relocation assistance for international candidates.
Quarterly team off-sites, hackathons, conferences and learning opportunities.
Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.
We recently raised $15mm in funding (total of $20mm raised) led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy (Eureka AI, Tesla, OpenAI), Tri Dao (Chief Scientific Officer of Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Huggingface), Emad Mostaque (Stability AI) and many others.
If you're excited about the opportunity to build the foundation for the future of decentralized AI and create a platform that empowers developers and researchers to push the boundaries of what's possible, we'd love to hear from you.
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