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About the Role

We are building a Large Physics foundation Model (LPM) to achieve general causal intelligence, capable of predicting and altering future events. Our mission is to learn physics from sensory observations to enable AI that can understand causality, predict, and control physical systems, starting with weather. We seek data engineers excited to tackle unsolved problems and contribute to the critical data curation process for our ML models.

Responsibilities

Own every dataset end to end, from discovery and access to ingestion pipelines and quality assurance.
Research and source new modalities of multimodal physical data (e.g., sparse sensors, point clouds, hyperspectral imagery, radar) and secure access.
Build petabyte-scale data pipelines (e.g., Apache Spark) for batch and streaming ingestion, including orchestration, storage, and monitoring.
Develop quality metrics for coverage, correctness, and consistency, and identify subtle inconsistencies.
Design and implement automated QA checks to continuously monitor data quality.
Write technical requirements and provide feedback to external data vendors and partners.
Collaborate with researchers to validate that new datasets improve model performance.

Requirements

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.
Ability to understand how data quality impacts model performance.
Comfortable going deep on unfamiliar source material, including reading format specifications, sensor documentation, and vendor manuals.
Experience working with external data vendors and partners, from technical evaluation to ongoing feedback.
Ability to own deliverables end-to-end, from collecting requirements to driving execution.

Skills

Apache Spark
Ray
Beam

Location

Remote

Work Type

Full-time

Experience Level

Mid-level

About the Company

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