Machine Learning Engineer II - Autonomous Driving & Inference Runtime

May Mobility is entering an exciting phase of growth as we expand our first-of-its-kind autonomous shuttle and mobility services across the nation. Launched in 2017 with a strong team of experienced roboticists and software engineers with decades of experience fielding robotic systems in the wild, May Mobility is looking to expand its team of robotics engineers with a background in robotics or autonomous vehicles.

We are seeking ML-Oriented Software Engineers with experience in robotics applications. As part of our Autonomous Driving ML team, you will use your knowledge of Software and Hardware concepts to deploy, optimize and scale State of the Art Machine Learning models for both Datacenter and Edge Vehicle devices.

• →Deploy and Optimize Machine Learning model architectures across May’s Autonomous Driving training and inference stacks.
• →Own the model-compilation and deployment pipeline end-to-end.
• →Establish and defend latency/throughput budgets across the AV stack, including profiling, regression and integrity tests.

Success in this role typically requires the following competencies:

• Architecting software for low-level GPU/CPU concurrency such as CUDA streams, pinned memory, kernel fusion and memory-layout optimization.
• Use of compilation and runtime utilities such as TensorRT, ONNX and torch.compile for edge deployments.
• Apply quantization, distillation, and pruning to fit models within onboard compute and memory budgets.

Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:

• Standard office working conditions which includes but is not limited to:
• Prolonged sitting
• Prolonged standing
• Prolonged computer use

Travel required? -  Low 5-10%

• Bachelor’s or Master’s degree in Robotics, Computer Science, Computer Engineering, or a related field with strong mathematical and engineering foundations.
• A minimum of 2 years writing software to interface with GPU and ML systems.
• Proficiency in C/C++/CUDA/PyTorch and experience in Linux environments.
• Familiarity with basic Perception and Planning concepts in Autonomous Driving.

• Familiarity with NVIDIA compute architectures (Ada, Hopper, Blackwell, etc).
• Familiarity with common profiling tools such as Nsight, Pytorch Profiler, flamegraph.
• Understanding of Quantization (INT8/FP8/FP16) and other compression techniques.
• Familiarity with NVIDIA DRIVEOS architecture and SoCs (Orin/Thor).
• Familiarity with techniques for scaling training throughput (batching, FSDP, streaming dataloaders).