Motional is seeking an ML Systems Engineer to build and optimize the core systems that enable researchers to train frontier models at scale. In this role, you will focus on improving training speed, cost, reliability, and throughput by working at the intersection of machine learning research and high-performance systems engineering. Your work will directly scale large-scale distributed model training and reduce time-to-convergence.
What You'll Do
- Utilize profiling tools like Nsight and PyTorch Profiler to identify bottlenecks in data loading, gradient computation, and communication.
- Implement optimizations such as kernel fusion, sharding, and tiling to improve step time.
- Optimize distributed training pipelines using frameworks like PyTorch Distributed.
- Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML workloads.
- Optimize robust data loading pipelines to maximize training throughput.
What We're Looking For
- A 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.
- 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.
Technical Stack
- Python, PyTorch
- Nsight, PyTorch Profiler
- Triton, CUDA
Team & Environment
You will be joining the ML Acceleration team.
Benefits & Compensation
- Compensation range: $144,000—$160,000 USD + potential company equity.
- Medical, dental, and vision insurance.
- 401k with a company match.
- Health saving accounts.
- Life insurance.
- Pet insurance.
Work Mode
This is a hybrid position based in Boston, Pittsburgh, or Las Vegas.
Motional AD Inc. is an EOE. We celebrate diversity and are committed to creating an inclusive environment for all employees.
