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Member of Technical Staff — Data Infrastructure

Causal Labs· Infrastructure
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San Francisco, SingaporeFull-timePosted 2mo ago

About the role

Data Infrastructure-focused role building the data platform underpinning a large physics foundation model, including storage, compute, and loading systems to enable cheap ingestion, fast querying, and immediate availability for 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

Qualifications

  • 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

Skills

Required:Large-scale data pipelinesDistributed compute systemsData ingestion and storage optimization
Last updated Oct 2, 2026. Interested? Apply on the company site.

About Causal Labs

Towards general causal intelligence

Causal Labs is building a Large Physics foundation Model (LPM) to learn causality through physics and weather

causalityphysicsweatherAIfoundation model
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