Senior 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 access and transform 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.
• Automate and resolve complex issues across the stack to maximize uptime and throughput for the ML fleet.

What we're looking for

• A B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience is required.
• Candidates must have a strong foundation in machine learning and hands-on experience across the end-to-end ML workflow.
• Experience building and delivering data and machine learning infrastructure in real-world production environments is required.
• Expertise in building and running large-scale distributed systems is required.
• Extensive programming experience in Java, Python, or Go is required.
• Familiarity with modern generative techniques like transformers, diffusion, and retrieval-augmented generation is required.
• Experience designing, building, and maintaining scalable, highly available systems that prioritize ease of use is required.
• Proficiency with ML frameworks (PyTorch, JAX, TensorFlow) and distributed data loading frameworks is preferred.