Machine Learning Infrastructure Engineer

Mind Robotics is building generalized physical AI to create robotic systems capable of adaptive work in industrial environments. They are seeking a Machine Learning Infrastructure Engineer to develop core systems that facilitate fast and scalable model training.

Responsibilities:

• Design and implement scalable systems for training large ML models
• Enable efficient workflows for data ingestion, training, and iteration
• Develop and optimize distributed training systems across hundreds of GPUs
• Implement strategies for parallelization, sharding, and efficient compute utilization
• Improve training efficiency through techniques such as attention optimizations, kernel fusion, and memory management
• Partner closely with modeling teams to accelerate iteration speed and reduce training costs
• Build internal tools for experiment tracking, monitoring, and debugging
• Implement systems for tracking training performance, failures, and resource utilization
• Debug and resolve bottlenecks across the training stack
• Provide lightweight infrastructure support for deploying and running models in production environments
• Optimize inference performance and reliability where needed
• Support core cloud infrastructure needs for training workloads (without heavy DevOps overhead)
• Manage compute resources efficiently across training jobs