Member of Technical Staff
Causal Labs
Member of Technical Staff
Causal Labs
Trains physics AI models to forecast weather
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. Physical observations arrive continuously, in many formats, at a scale that dwarfs what is used to train today's LLMs. Your mission is to build the data platform underneath it all — the storage, compute, and loading systems that make every dataset cheap to ingest, fast to query, and immediately available to training.
Responsibilities
Design and operate petabyte-scale storage: lakehouse architecture, file formats, and data layout optimized for both batch and real-time queries
Own the shared compute and orchestration platform (e.g. Spark, Ray, workflow scheduling) that ingestion and research pipelines run on
Optimize data strategy end to end from storage to loading, owning high-throughput data loading into training up to the tensor boundary
Build systems for cataloging, deduplication, lineage, search, and reproducibility at every stage of the data lifecycle
Implement the platform-level quality and monitoring tooling that data and research teams build their checks on
Scale infrastructure to improve engineering velocity and ensure reliability, with monitoring and alerting to match
Work across the full data lifecycle when the mission needs it — including building and operating ingestion pipelines for critical data sources directly
What we're looking for
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 and distributed compute systems (e.g. Spark, Ray, Beam)
Knowledge of state-of-the-art methods and tools for data ingestion, storage, and loading — including file formats and storage systems (e.g. Parquet, Zarr, Delta Lake) and how they impact performance and scalability
Deep familiarity with cloud infrastructure, data lake architectures, and batch and streaming pipelines
Understanding of how data loading throughput affects large-scale training, and experience optimizing it
Owns deliverables end-to-end, from collecting and translating requirements to autonomously driving execution
Trains physics AI models to forecast weather
Causal Labs is an AI research company building a foundation model of physical systems for industries whose operations turn on the weather. The company trains on atmospheric sensor and observation data rather than text or images, a domain where cause and effect are governed by physics and yesterday's prediction can be scored against what happened. It runs early pilots with commercial partners and pairs researchers with engineers who deploy the models on customer data. Unlike conventional forecasters, which report what conditions are likely, it is also trying to identify which interventions would change an outcome. The company's goal is to make AI reason about cause and effect by grounding it in the most heavily observed physical system available.
Explore more opportunities at Causal Labs