Machine Learning Infrastructure Engineer

At Mind Robotics, we’re building generalized physical AI—robotic systems capable of dexterous, adaptive, and reasoning-intensive work in real-world industrial environments. Our ability to iterate quickly on large-scale models depends on world-class ML infrastructure.

We’re looking for a Machine Learning Infrastructure Engineer to build the core systems that enable fast, reliable, and scalable model training—powering everything from experimentation to production deployment.

• 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

• 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

• Experience supporting inference systems in production

• Familiarity with robotics or embodied AI workloads

• Experience building tools for experiment management and researcher productivity