Lead Machine Learning Engineer

May Mobility develops autonomous vehicle technology to create safer, greener, and more accessible transportation. The Lead Machine Learning Engineer will enhance machine learning capabilities for autonomous driving, simulation, and ML platform systems by designing and evaluating advanced models, leading engineering teams, and establishing data and training practices at petabyte scale.

Responsibilities:

• Design, train and evaluate state of the art models for May’s autonomous driving, simulation and ML Platform stack
• Leverage emerging techniques in the End-to-End driving, Vision Language Action (VLA), World or Foundation model domains to solve commercial-scale problems
• Lead small teams of cross functional Engineers beyond the state of the art
• Define data balance, training experiment and evaluation practices to train efficiently at petabyte scale

Requirements:

• Direct experience architecting & training VLA, MMLM, or Generative World Models for commercial-scale applications
• Experience composing, processing and characterizing large (>100TB) multi-modal datasets
• Experience analyzing and addressing long-tail failure cases in large models
• Experience leading teams of 2-3 Engineers and communicating technical details to interdisciplinary leadership
• Extensive practical experience in one of the following domains: Vision Language Action Models, Generative World Models, Foundation Models in Robotics, Data Centric AI
• A minimum of 4 years of industry experience working on commercial robotics systems
• A minimum of 1 year mentoring ML Engineers in a commercial or lab environment
• Master's degree in Robotics, Computer Science, or Computer Engineering, or a field that requires a strong mathematical and/or engineering foundation
• Practical experience handling the “Long Tail” problem in Machine Learning
• Strong programming skills in Python/PyTorch in a Linux environment
• Functional understanding of LiDAR, Camera and Radar processing techniques
• PhD and/or published research in the described specialty domains
• Familiar with common post-training techniques
• Experience deploying models to resource constrained and edge hardware
• Functional understanding of C/C++/CUDA memory and threading models