Member of Technical Staff - ML Infra

• Design, deploy, and maintain large distributed ML training and inference clusters
• Develop efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and model training throughout the entire ML lifecycle
• Research and test various training approaches including parallelization techniques and numerical precision trade-offs across different model scales
• Analyze, profile and debug low-level GPU operations to optimize performance

This role contributes to advancing machine learning infrastructure, enabling more efficient and scalable AI development, which can support various impactful applications across different domains.

• Strong grasp of state-of-the-art techniques for optimizing training and inference workloads
• Demonstrated proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed) to train large foundation models
• Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings
• Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker)

No specific benefits, compensation, or perks mentioned in the job description.

No information provided about the organization's mission and work in the job description.