Research Engineer, Robot Foundation Models

The role

A Research Engineer will build core systems for fast, reliable, and scalable model training. This role involves developing distributed training systems and optimizing training efficiency for large ML models.

What you will do

• 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.

Required qualifications

• Strong experience building infrastructure for large-scale ML training.
• Deep understanding of how modern LLM/VLM systems are trained and scaled.
• Proven experience setting up and scaling distributed training across hundreds of GPUs.
• Strong understanding of parallelization strategies (data, model, pipeline parallelism).
• Strong proficiency in Python programming.
• Expert-level proficiency in PyTorch and/or JAX.
• Strong understanding of techniques like attention optimization, kernel fusion, and efficient memory usage.

Nice to have

• Experience supporting inference systems in production.
• Familiarity with robotics or embodied AI workloads.
• Experience building tools for experiment management and researcher productivity.

About the company

Mind Robotics is building Physical AI for real-world industrial deployment, starting with the factory floor.