About us

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.

Responsibilities

• Design, deploy, and maintain large distributed ML training and inference clusters

• Develop efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and model training throughout the entire ML lifecycle

• Research and test various training approaches including parallelization techniques and numerical precision trade-offs across different model scales

• Analyze, profile and debug low-level GPU operations to optimize performance

• 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 state-of-the-art techniques for optimizing training and inference workloads

• Demonstrated proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed) to train large foundation models

• Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings

• Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker)

• Background working on distributed task management systems and scalable model serving & deployment architectures

• Understanding of monitoring, logging, observability, and version control best practices for ML systems

You don’t have to meet every single requirement above.

Benefits

• Work on deeply challenging, unsolved problems

• Competitive cash and equity compensation

• Medical, dental, and vision insurance

• Catered lunch & dinner

• Unlimited paid time off

• Visa sponsorship & relocation support