Mind Robotics

Research Engineer

About

The Role 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 Research Engineer to build the core systems that enable fast, reliable, and scalable model training—powering everything from experimentation to production deployment. 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 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

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