Research Engineer - Distributed Training

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 (PyTorch Distributed, DeepSpeed, MosaicML’s LLM Foundry), and tools (Ray) Experience in large-scale model training including data, tensor & pipeline parallelism Solid understanding of MLOps best practices (model versioning, experiment tracking, CI/CD) Passion for advancing decentralized AI model training and democratizing AI capabilities

Lead and participate in novel research for decentralized training orchestration Optimize AI workload performance, cost, and resource utilization Contribute to open-source libraries and frameworks for distributed model training Publish research in top-tier AI conferences Distill technical project outcomes into layman approachable technical blogs Stay up-to-date with AI/ML infrastructure, tools, and decentralized training research