Machine Learning - Infrastructure

About the Company

Causal Labs is a team of researchers and engineers from self-driving, drug discovery, and robotics - including Google DeepMind, Cruise, Waymo, Insitro, and Nabla Bio - who believe general causal intelligence will be the most important technical breakthrough for civilization. Our mission is general causal intelligence, AI that is capable of predicting the future and identifying the optimal actions to change that future.

Responsibilities

• 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
• Stay up-to-date on research to bring new ideas to work

Requirements

• Strong grasp of state-of-the-art techniques for optimizing training and inference workloads
• Demonstrated proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed)
• Knowledge of cloud platforms (GCP, AWS, or Azure)
• Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker)
• Background working on distributed task management systems and scalable model serving & deployment architectures
• Understanding of monitoring, logging, observability, and version control best practices for ML systems