Member of Technical Staff — ML Data Infra

Design, build, and operate large-scale data pipelines for ingestion, processing, filtering, and curation of multimodal training data (video, audio, text); translate research-grade data processing code into robust production pipelines; optimize pipeline throughput and efficiency at scale; build and maintain data quality systems (deduplication, filtering, validation, quality scoring); manage petabyte-scale datasets (storage architecture, versioning, lineage tracking, cost efficiency); collaborate with researchers to translate data requirements into scalable processing systems; build tooling and infrastructure to enable faster research cycles. Required strengths include proven production data-pipeline experience, proficiency with distributed frameworks (Spark, Ray, Dask), strong software engineering fundamentals, and familiarity with multimodal data and data-quality considerations; ability to ship production versions rapidly from prototypes.