Senior ML Engineer - Robotics Autonomy & Large-Scale Training

United States Digital Space LLC; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

United States Digital Space LLC is seeking a highly skilled ML engineer to scale multimodal training systems for autonomous robotics. The role emphasizes distributed training across GPU clusters, optimization of data pipelines, and collaboration with ML researchers to deploy high-performance autonomy models.

You will work with leaders in software, hardware, and design to advance robotics data processing and end-to-end model iteration, ensuring efficient training and scalable production readiness.

Qualifications

• Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline.
• Minimum of 5 years of professional experience developing, training, and deploying machine learning models in production environments.
• Hands-on experience training ML models across multiple GPUs or compute nodes with distributed frameworks.

Responsibilities

• Design and maintain large-scale training systems for petabyte-scale multimodal data across GPU clusters.
• Identify bottlenecks in data loading, preprocessing, and inter-node communication to maximize GPU utilization.
• Develop neural networks for autonomy tasks handling high-dimensional sensor data.
• Create loss functions and training strategies to improve autonomy performance.
• Configure, monitor, and maintain distributed training jobs across machines and GPUs.
• Preprocess, transform, and augment robotics datasets for model training.
• Collaborate with ML researchers and engineers to integrate new models into production pipelines.
• Analyze metrics and logs to guide improvements in architecture and data usage.

Skills

Python

Distributed training

GPU clusters

Neural networks

Model deployment

Education

Master's or PhD in CS/Robotics/ML

Tools

PyTorch

TensorFlow

Job description

United States Digital Space LLC is seeking a highly skilled ML engineer to scale multimodal training systems for autonomous robotics. The role emphasizes distributed training across GPU clusters, optimization of data pipelines, and collaboration with ML researchers to deploy high-performance autonomy models.

You will work with leaders in software, hardware, and design to advance robotics data processing and end-to-end model iteration, ensuring efficient training and scalable production readiness.