Member of Technical Staff — Data Ingestion & Quality

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

• Demonstrated experience building large-scale data pipelines, QA systems, or evaluation workflows (e.g. Spark, Ray, Beam)
• Detail-oriented in identifying subtle data inconsistencies and issues that could affect quality, with the ability to understand how quality impacts model performance
• Comfortable going deep on unfamiliar source material — reading format specifications, sensor documentation, and vendor manuals to get ingestion exactly right
• Experience working with external data vendors and partners, from technical evaluation to ongoing feedback
• Owns deliverables end-to-end, from collecting and translating requirements to autonomously driving execution

Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.

We look for data engineers who are excited to tackle unsolved problems. Data is critical to any ML model but is especially consequential for our thesis to learn physics from sensory observations. The vast majority of meaningful progress in AI comes not from new architectures, but from training on data that is carefully curated with specific characteristics, quality, and scale.