Staff Machine Learning Engineer

What you'll do

• Design and build infrastructure for petabyte-scale data ingestion, versioning, and lineage across multimodal datasets.
• Develop Python SDKs and core libraries to help ML engineers transform and load model-ready data.
• Build high-throughput data loading primitives to ensure GPU workloads remain compute-bound rather than I/O-bound.
• Operate distributed data pipelines using Spark, Daft, and Rust-based systems for large-scale preparation.
• Optimize platform components for seamless integration with PyTorch, JAX, and TensorFlow frameworks.
• Implement governance features including legal terms enforcement, privacy controls, and end-to-end data lineage.
• Support next-generation workloads such as foundation models, multimodal data, and retrieval-augmented systems.
• Diagnose and automate complex issues across the stack to maximize uptime and throughput for the ML fleet.

What we're looking for

• B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience.
• Strong foundation in machine learning with hands-on experience across the end-to-end ML workflow including data preparation, pipeline development, and deployment.
• Expertise in building and running large scale distributed systems.
• Proven experience building and delivering data and machine learning infrastructure in real-world production environments.
• Experience designing, building, and maintaining scalable, highly available systems that prioritize ease of use.
• Extensive programming experience in Java, Python, or Go.
• Familiarity with modern generative techniques, fine-tuning workflows, model optimization, and large scale production environment troubleshooting.
• Proficiency with PyTorch, JAX, TensorFlow, columnar formats (Parquet, Iceberg), or distributed data loading frameworks (preferred).