Research Engineer - Distributed Training

Prime Intellect; United States; Hybrid; USD 110,000 - 150,000; Full time

Benefits offered by this job

Competitive compensation including equity incentives

Flexible work arrangements

Visa sponsorship and relocation assistance

Quarterly team off-sites and hackathons

Opportunity to work with a mission-driven team

Job summary

A cutting-edge AI company is seeking a Research Engineer specializing in Distributed Training. The candidate will be responsible for leading research to develop a decentralized AI training orchestration solution while optimizing workloads for performance and cost. Ideal applicants will have a strong AI/ML engineering background, expertise in distributed training frameworks, and a passion for advancing AI capabilities. The company offers competitive compensation, flexible work arrangements, and opportunities for professional growth.

Qualifications

• Strong background in AI/ML engineering with experience in end-to-end pipelines for large-scale AI models.
• Expertise in distributed training techniques, frameworks like PyTorch Distributed, and tools like Ray.
• Solid understanding of MLOps best practices including model versioning and CI/CD pipelines.

Responsibilities

• Lead research to build a decentralized training orchestration solution.
• Optimize AI workloads using recent compute & memory optimization techniques.
• Contribute to open-source libraries for distributed model training.
• Publish research in top-tier AI conferences such as ICML & NeurIPS.
• Distill technical outcomes into approachable blogs for customers.
• Stay up-to-date with advancements in AI/ML infrastructure.

Job description

Research Engineer - Distributed Training at Prime Intellect

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 will play a crucial role in shaping our technological direction, focusing on decentralizing AI training. 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 in 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 with large‑scale model training, including data, tensor, & pipeline parallelism.
• Solid understanding of MLOps best practices, including model versioning, experiment tracking, and 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 are not familiar with these, but feel you can contribute to our mission and you’re a high‑energy person, please reach out— we offer resources to help you get started.

Benefits & Perks

• 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 $15 million in funding (total of $20 million 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.