Research Engineer - Distributed Training at Prime Intellect

**Who this is for** Prime Intellect is looking for a Research Engineer focused on Distributed Training to help build their decentralized AI training stack. This

Work type: remote

Location: San Francisco | Remote

Salary: $150,000 – $300,000/yr

Type: Full-time

Summary

**Who this is for** Prime Intellect is looking for a Research Engineer focused on Distributed Training to help build their decentralized AI training stack. This role is for individuals who are passionate about scaling AI training and maximizing its efficiency. **Key highlights** You will lead research efforts to build a massive-scale, reliable, and secure decentralized training orchestration solution. Responsibilities include optimizing AI workloads for performance and cost, contributing to open-source frameworks, and publishing research in top AI conferences. **You might be a good fit if you...** - Have extensive experience designing and implementing end-to-end pipelines for training large-scale AI models. - Possess deep expertise in distributed training techniques and frameworks (e.g., PyTorch Distributed, DeepSpeed, MosaicML’s LLM Foundry). - Have experience with large-scale model training, including various parallelism techniques. - Understand MLOps best practices for model versioning and CI/CD.

Job Description

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.

## 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.

• 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](https://a.co/d/frW8MHY), [here](https://a.co/d/4WRhR0Y) and [here](https://github.com/stas00/ml-engineering/tree/master)) and please reach out!

## Benefits & Perks

• Cash Compensation Range of $150-300k, plus 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.

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.

View this job on nocollar jobs