Machine Learning Systems Engineer

Motional is a driverless technology company developing autonomous vehicles and transportation technologies. The Machine Learning Systems Engineer will build and optimize the core systems that enable large-scale distributed model training, improving speed, cost, reliability, throughput, and time-to-convergence. The role focuses on performance profiling, distributed training, GPU kernel development, and data pipeline engineering.

Responsibilities:

• Performance Profiling & Optimization : Utilize profiling tools (e.g., Nsight, PyTorch Profiler) to identify bottlenecks in data loading, gradient computation, and communication. Implement optimizations like kernel fusion, sharding, and tiling to improve step time
• Distributed Training : Optimize distributed training pipelines using frameworks such as PyTorch Distributed
• Kernel Development : Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads
• Data Pipeline Engineering : Optimize robust data loading pipelines that maximize training throughput

Requirements:

• Education : Bachelor's, Master's degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline
• Software Engineering : Strong proficiency in Python
• ML Frameworks : Extensive hands-on experience with PyTorch
• ML Knowledge : Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes
• Problem Solving : Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges