Note: The job is a remote job and is open to candidates in USA. Motional is a driverless technology company developing autonomous vehicles. The Machine Learning Systems Engineer will build and optimize core systems for large-scale distributed model training, focusing on speed, cost, reliability, and throughput. The role involves performance profiling, distributed training optimization, 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
Skills
- Bachelor's, Master's degree, or PhD in Computer Science, Computer Engineering, or a related technical discipline
- Strong proficiency in Python
- Extensive hands-on experience with PyTorch
- Experience optimizing machine learning model execution during training and inference, alongside a strong understanding of fundamental machine learning concepts, architectures, and processes
- Exceptional analytical and problem-solving skills, with a bias for action and a data-driven approach to technical challenges
- We encourage a hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas to support collaboration, or this role can be fully remote
Benefits
- Candidates for certain positions are eligible to participate in Motional’s benefits program.
- Medical
- Dental
- Vision
- 401k with a company match
- Health saving accounts
- Life insurance
- Pet insurance
- Hybrid schedule with in-office time at one of our locations in Boston, Pittsburgh, or Las Vegas, or fully remote
Company Overview
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