May Mobility
Director, Autonomy Behavior
$238K–$298K
Ann Arbor, MI - Onsite +1 · Director · Full-time · Posted Jul 22, 2026
- Location
- Ann Arbor, MI - Onsite
Ann Arbor, Michigan, United States - Experience
- 7+ years of software-focused engineering experience; experience leading a team of 10+ engineers. Prior individual-contributor experience developing and deploying solutions in the autonomous vehicle or related space. Desirable: 5+ years of management experience.
- Education
- Bachelor's degree in engineering in a highly technical field (e.g. robotics, computer science, computer engineering, aerospace engineering, physics) or equivalent experience.
About the job
Leads the Behavior & Controls team’s strategy, technical roadmap, and delivery of autonomy behavior and control technology for May Mobility’s autonomous vehicles.
What you'll do
- Lead the team and set the vision, strategy, and roadmap for advancing MPDM technology.
- Coordinate with autonomy, vehicle engineering, software, data, infrastructure, and operations teams to solve and deploy autonomy solutions.
- Oversee commercial performance, issue resolution, KPIs, hiring, and development of the team.
What they ask for
- Bachelor's degree in a highly technical engineering field or equivalent experience.
- 7+ years of software-focused engineering experience solving challenging robotics problems in mission-critical software and/or robotic systems.
- Experience leading a team of 10+ engineers.
- Prior individual-contributor experience developing and deploying innovative solutions in the autonomous vehicle or related space.
Nice to have
- PhD or master’s degree in a relevant technical field; 5+ years of management experience.
- Experience developing and/or using MPDM; deep understanding of autonomous vehicle challenges and the state of the art in autonomy.
Pay
$238,000 — $298,000 USD (base salary)
Tools
Multi-Policy Decision Making (MPDM), reinforcement learning
AI focus
Advances autonomous driving decision-making and behavior technology, including data-driven policies, MPDM simulations, learned reward functions, policies, and control algorithms.
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