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Member of Technical Staff — Data Ingestion & Quality

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

About the role

Data Engineer focused on end-to-end data ingestion, quality, and pipeline orchestration for multimodal physical datasets to train a Large Physics foundation Model.

Responsibilities

  • Research and source new modalities of multimodal physical data (e.g. sparse sensors, point clouds, hyperspectral imagery, radar), and secure access through partnerships, vendors, and public archives
  • Build petabyte-scale data pipelines (e.g. Apache Spark) that ingest each source into storage in standardized, training-ready form, across both batch and streaming — including orchestration, storage, and monitoring where platform infra is lacking
  • Develop quality metrics that measure coverage, correctness, and consistency across sources — catching subtle inconsistencies (sensor bias, drift, processing artifacts) that degrade models
  • Design and implement automated QA checks that continuously measure and monitor data quality over time, and own the verdicts they produce
  • Write technical requirements and provide actionable feedback to external data vendors and partners
  • Collaborate with researchers to validate that new and improved datasets translate into model performance

Qualifications

  • 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 understanding of 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

Skills

Required:Large-scale data pipeline developmentQuality metrics designAutomated QA for dataExternal data vendor collaboration

Benefits

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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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