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.

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.

Member of Technical Staff - ML Research

About the Job

About the Company

Causal Labs

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