10+ years of work experience in machine learning infrastructure, distributed data systems, or a related field. 10+ years of experience building and shipping large-scale data or ML infrastructure and platforms in production. Extensive experience architecting and delivering large-scale distributed data or ML infrastructure that multiple teams or products depend on in production. A track record of setting technical direction and driving it to delivery across teams, not just within a single component.
Deep systems engineering: strong Python plus a systems language (Rust strongly preferred; C++ or Go acceptable), and hands-on performance engineering for I/O-bound workloads (Arrow, zero-copy, memory mapping, async I/O, high-throughput object storage). Deep familiarity with columnar and lakehouse formats (Parquet, Iceberg, Delta, or Lance) and the judgment to choose between them at scale. Strong working knowledge of the end-to-end ML workflow and how training and inference consume data, enough to architect data systems that serve them.
Familiarity with modern ML and generative techniques (transformers, diffusion, retrieval-augmented generation, fine-tuning) at the level needed to design for those consumers. Demonstrated ability to design highly available, easy-to-use systems and to mentor and elevate the engineers around you. Strong collaboration and communication, with the ability to align multiple teams around a technical direction. B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience.