Data Engineer: End-to-End Physics Data Pipelines
Causal
Causal, a San Francisco startup, is building a Large Physics foundation Model to learn physics from sensory data. We seek data engineers to own datasets end-to-end—from source discovery to training-ready pipelines and access controls—ensuring data quality for multimodal physical data. You will design scalable pipelines using Spark, Ray, and Beam, develop QA checks, and collaborate with researchers to verify that datasets translate into model performance. #J-18808-Ljbffr Causal
Vacancy posted 4 days ago
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