Causal Labs' mission is to build safe, steerable AI physics models, starting with accurate weather prediction and control.Our team is small and driven by deep passion and a sense of urgency for solving the most important problems facing humanity. We've previously built and led the development of safety-critical models in robotics and autonomous vehicles. We're funded by leading investors in Silicon Valley and are lucky to have the support of amazing advisors &amp; angels from institutions like OpenAI, Google DeepMind, Cruise, Waymo, and many more.

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Focus: Mitigation or Adaptation

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Climate Solutions

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AI Physics Models to Predict &amp; Control the Weather

Responsibilities<ul><li>Work across the full ML stack (data, model, eval, and infrastructure)</li><li>Implement novel model architectures and training algorithms</li><li>Build data pipelines and training infrastructure for massive, petabyte-scale, multimodal datasets</li><li>Rapidly iterate on experiments and ablations</li><li>Stay up-to-date on research to bring new ideas to work</li></ul>What we’re looking forWe value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.<ul><li>Strong grasp of machine learning fundamentals, and depth in at least one core domain (e.g. Computer Vision, Sensor Fusion, Language Models, Physics-informed NNs)</li><li>Experienced at training models and understanding experiment results through careful analysis and ablation studies.</li><li>Experienced at writing and optimizing massive petabyte-scale data pipelines.</li><li>Familiarity with distributed training.</li><li>[bonus] Familiarity with meteorology, computational fluid dynamics, and/or numerical simulations.</li></ul>You don’t have to meet every single requirement above.

Member of Technical Staff - ML Research

Responsibilities<ul><li>Design, deploy, and maintain large distributed ML training and inference clusters</li><li>Develop efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and model training throughout the entire ML lifecycle</li><li>Research and test various training approaches including parallelization techniques and numerical precision trade-offs across different model scales</li><li>Analyze, profile and debug low-level GPU operations to optimize performance</li><li>Stay up-to-date on research to bring new ideas to work</li></ul>What we’re looking forWe value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.<ul><li>Strong grasp of state-of-the-art techniques for optimizing training and inference workloads</li><li>Demonstrated proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed) to train large foundation models</li><li>Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings</li><li>Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker) </li><li>Background working on distributed task management systems and scalable model serving &amp; deployment architectures</li><li>Understanding of monitoring, logging, observability, and version control best practices for ML systems</li></ul>You don’t have to meet every single requirement above.

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