Machine Learning - Infrastructure

Our 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.

We look for infrastructure engineers who are excited to tackle unsolved problems. Our training and inference challenges demand deep expertise in setting up distributed training clusters and optimizing performance for large models. If you have experience building large-scale ML infrastructure in related fields such as language and vision models, robotics, biology -- join us on this mission.

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