Key skills
Machine LearningC++ DevelopmentEmbedded SystemsGPU ProgrammingMLOpsCollaborationProblem SolvingAdaptabilityC++CMLDeep LearningPyTorchTensorFlow
About this role
Motional is a driverless technology company making autonomous vehicles a safe, reliable, and accessible reality. In this role, you will improve the compute performance of autonomous driving systems and work on optimizing ML models and software for embedded compute platforms.
Responsibilities:
- Focus deeply on ML model deployment, integration of multiple ML models, and ML model optimization on embedded compute platforms
- Dive deep into the full ML software stack. Analyze ML workload performance on a variety of hardware processors, optimize ML models, improve ML software, and help us continually improve our stack through the application of efficient and effective ML approaches
- Design, develop, test, integrate, and optimize software and tools on a variety of ML compute architectures
- Collaborate with deep learning experts in perception, prediction, and other autonomous driving application areas to enable algorithms on GPU, NPU, and other ML accelerator architectures
- Optimize the utilization of GPU/NPU resources and sharing of GPU/NPU access across multiple programs running on the same system
- Lead designs to determine the needs of the system and how to best meet those needs through continually improving our ML software stack
- Advise peers and management on technical matters
Requirements:
- Experience with machine learning accelerators, including GPUs, NPUs, TPUs, and their programming environments, including CUDA, TensorRT, or similar technologies
- Strong experience with modern C++ development in a Linux environment
- Experience with parallel and high-performance computing
- Comfortable with experimentation and evaluating different options as we work towards finding solutions that work
- A degree in Software Engineering, Computer Science, Electrical or Electronic Engineering, or similar technical field of study, or you have equivalent knowledge gained through your practical experience
- Experience with PyTorch, TensorFlow, ONNX, and/or other ML frameworks
- Experience with embedded systems development for ARM-based system-on-chip architectures
- Experience working in a MLOps or DevOps environment
- Passion for self-driving technology and its potential for positive impact on the world