Member of Technical Staff - Data Infrastructure

- 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