About the job
At Causal Labs, we are on a groundbreaking mission to develop general causal intelligence, harnessing AI to (1) forecast future events and (2) pinpoint optimal actions to influence that future.
To realize this vision, we are constructing a Large Physics foundation Model (LPM), as the domains governed by physics inherently feature cause-and-effect relationships, which is distinct from visual or textual data.
Weather serves as the perfect training environment for our LPM, being the most extensively observed physical system and providing rapid, objective ground truth feedback from sensory data at an unprecedented scale, far exceeding what is utilized for current large language models (LLMs).
Our team comprises elite researchers and engineers with backgrounds in self-driving technology, drug discovery, and robotics, including talents from Google DeepMind, Cruise, Waymo, Meta, Nabla Bio, and Apple. We believe that achieving general causal intelligence will be a pivotal technological advancement for humanity.
We are searching for infrastructure engineers who are eager to tackle formidable challenges and contribute to our mission.
Your expertise in distributed training clusters and performance optimization for large models will be crucial as we address our training and inference challenges. If you possess experience in developing large-scale ML infrastructure within fields like language models, vision systems, robotics, or biology, we invite you to join us.