Research Engineer - Distributed Training

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 (eclipse) 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

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