Staff ML Infrastructure Engineer

What you'll do

• Define architecture for data ingestion, versioning, lineage, governance, and delivery across structured, unstructured, and multimodal data.
• Set technical direction for platform formats, dataset abstractions, SDKs, and interfaces across partner teams.
• Lead design reviews, mentor engineers, resolve system-level problems, and shape the roadmap for next-generation ML workloads.

What they ask for

• 10+ years of work experience in ML infrastructure, distributed data systems, or a related field, including 10+ years building and shipping large-scale data or ML infrastructure in production.
• Extensive experience architecting production distributed data or ML infrastructure used by multiple teams or products, and setting technical direction across teams.
• Strong Python and a systems language (Rust preferred; C++ or Go acceptable), with hands-on performance engineering for I/O-bound workloads.
• Deep familiarity with columnar and lakehouse formats; strong knowledge of ML workflows and generative techniques; B.S., M.S., or Ph.D. in a stated field, or equivalent practical experience.

Nice to have

• Experience defining data or ML platform architecture adopted across an organization.
• Experience with ML data-loading frameworks, GPU or TPU fleets, lineage and governance systems, data-processing internals, or Docker and Kubernetes.

Pay

$184,700 and $324,800 (base salary)

Tools

Python, Rust, C++, Go, Arrow, Parquet, Iceberg, Delta

AI focus

Builds infrastructure for foundation models, generative AI, multimodal data, embeddings, retrieval-augmented systems, training, and inference.