Lead Machine Learning Engineer, May Mobility

• 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
• Standard office working conditions which includes but is not limited to:
• Prolonged sitting
• Prolonged standing
• Prolonged computer use
• Don’t meet every single requirement? Studies have shown that women and/or people of color are less likely to apply to a job unless they meet every qualification. At May Mobility, we’re committed to building a diverse, inclusive, and authentic workforce, so if you’re excited about this role but your previous experience doesn’t align perfectly with every qualification, we encourage you to apply anyway! You may be the perfect candidate for this or another role at May

Desirable

• We are seeking Machine Learning Leaders in the Autonomous Vehicle domain. As part of our team, you will play a critical role in enhancing May’s Machine Learning capabilities both on and off the vehicle, in a commercial large-scale environment with high standards of quality
• 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