Senior ML Engineer - Autonomous Driving Systems

May Mobility, Inc.; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

May Mobility, Inc. is seeking exceptional ML leaders in the autonomous vehicle domain to design, train and evaluate state-of-the-art models for vehicle perception, planning, and ML platform stacks.

You will drive innovation in VLA and World Models while guiding small cross-functional teams in a performance-driven, edge-enabled environment. You will mentor ML engineers, address long-tail failure modes, and shape data-centric AI strategies for commercial robotics systems with real-world impact in

Qualifications

• Extensive practical experience in Vision Language Action Models, Foundation Models in Robotics, or Data Centric AI.
• 4+ years of industry experience on commercial robotics systems.
• 1 year mentoring ML Engineers in a commercial or lab environment.
• Master’s degree in Robotics, Computer Science, or Computer Engineering, or a related field.

Responsibilities

• Design, train and evaluate state-of-the-art models for autonomous vehicle and ML platform tasks.
• Leverage emerging techniques in End-to-End driving, VLA, and World Models to solve commercial-scale problems.
• Lead small teams of cross-functional engineers beyond the state of the art.
• Define data balance, training experiments and evaluation practices for petabyte-scale data.

Skills

Vision Language Action

Foundation Models Robotics

Data Centric AI

Python PyTorch

LiDAR Camera Radar

Edge Deployment

Mentoring ML Engineers

Education

Master’s degree in Robotics/CS/Engineering

PhD with publications

Tools

C/C++/CUDA

Job description

May Mobility, Inc. is seeking exceptional ML leaders in the autonomous vehicle domain to design, train and evaluate state-of-the-art models for vehicle perception, planning, and ML platform stacks.

You will drive innovation in VLA and World Models while guiding small cross-functional teams in a performance-driven, edge-enabled environment. You will mentor ML engineers, address long-tail failure modes, and shape data-centric AI strategies for commercial robotics systems with real-world impact in