ML Infrastructure Engineer

You will build systems across the robot learning pipeline, from multimodal data ingestion through distributed model training and real-time inference. You will maintain research infrastructure, optimize performance, manage datasets, and create tooling for debugging, visualization, and experiment analysis.

Responsibilities

• Maintain a research codebase for fast iteration and correctness
• Own model-training infrastructure, including scheduling, checkpointing, metrics, and logging
• Scale distributed training across GPU clusters
• Optimize memory usage and training throughput
• Build low-latency inference pipelines for real-time robot control
• Design pipelines for ingesting, validating, and transforming multimodal robot data
• Build storage and metadata indexing systems for datasets
• Build research tooling for debugging, visualization, and experiment analysis

Requirements

• Software engineering and systems fundamentals
• Experience building distributed systems or large-scale data pipelines
• Experience with ML training infrastructure
• Ability to reason about performance, memory, I/O, and GPU utilization
• Experience managing training workloads with SLURM, Kubernetes, or similar systems
• Ability to design, build, operate, and iterate on systems end to end

Benefits

• Equity