Machine Learning - Research
CausalOur mission is general causal intelligence, AI that is capable of (1) predicting the future and (2) identifying the optimal actions to change that future.
To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because domains governed by physics have inherent cause and effect relationships, unlike visual or textual data.
Weather is the ideal training ground for an LPM. It is the most well-observed physical system, offering rapid, objective ground truth feedback from sensory observations and data at a scale that dwarfs what is used to train today’s LLMs.
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.
We look for researchers who are excited to tackle unsolved problems.
Our research challenges offer an opportunity to build powerful models grounded in observable feedback and verifiable ground truth. If you have experience doing frontier research and training large-scale models from scratch in related fields such as language and vision models, robotics, biology – join us.
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
What we’re looking for
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)
Experience training models and an ability to understand experiment results through careful analysis and ablation studies.
Experienced at writing and optimizing massive petabyte-scale data pipelines.
Familiarity with distributed training and inference.
[bonus] Familiarity with meteorology, computational fluid dynamics, and/or numerical simulations.
You don’t have to meet every single requirement above.
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