Owner of developer-facing content and reference pipelines for ML training data workflows. Produce cookbooks, benchmarks, demos using real datasets, advocate internally for developer needs, answer user questions, and represent the company at conferences and workshops.

Build and optimize low-level, high-performance software and cloud-scale storage systems for ML-focused data infrastructure. Design data layouts, compression strategies, and orchestration layers; drive performance engineering and systems-level improvements. Collaborate with a small team in New York or London, leveraging open-source tools and cloud warehouse architectures.