Machine Learning - Research

Causal Labs•San Francisco, CA

11d

About The Position

Our mission is to build causal intelligence, starting with physics models to predict and control the weather. We're building a small team driven by a deep passion and urgency to solve this civilizationally important problem. Our founding team has led & shipped models across self-driving cars, humanoid robotics, protein folding, and video generation at world-class institutions including Google DeepMind, Cruise, Waymo, Meta, Nabla Bio, and Apple.

Requirements

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
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)
Experienced at training models and understanding experiment results through careful analysis and ablation studies.
Experienced at writing and optimizing massive petabyte-scale data pipelines.
Familiarity with distributed training and inference.

Nice To Haves

Familiarity with meteorology, computational fluid dynamics, and/or numerical simulations.
You don’t have to meet every single requirement above.

Responsibilities

Work across the full ML stack (data, model, eval, and infrastructure)
Implement novel model architectures and training algorithms
Build data pipelines and training infrastructure for massive, petabyte-scale, multimodal datasets
Rapidly iterate on experiments and ablations
Stay up-to-date on research to bring new ideas to work