The threat model here is physical. May Mobility builds autonomous shuttles that operate on public roads and transit routes, partnering with municipalities, transit agencies, and ride-hailing platforms like Lyft and Uber. Founded in 2017, the company has delivered over 300,000 autonomy-enabled rides across deployments in the United States and Japan, including operations in Minnesota and Tokyo. Every mile is a live-fire test of real-time AI systems running reinforcement learning and multi-policy decision-making stacks under adversarial conditions - sensor spoofing, edge-case traffic scenarios, fleet-wide coordination over potentially degraded networks.
The technical surface area spans autonomous vehicle perception pipelines, real-time AI inference, and the reinforcement learning models that govern on-road decision making. Security here isn't about protecting data in a vacuum; it's about ensuring the integrity of sensor inputs, model weights, and vehicle-to-infrastructure communication against actors who can cause kinetic harm. The company operates in transportation, urban mobility, and public transit verticals, which means compliance regimes, municipal data-sharing agreements, and platform integrations all expand the attack surface.
May Mobility frames its mission around safer, greener, and more accessible cities, with an explicit emphasis on equity in urban mobility. For security engineers, the draw is the stakes: you're defending systems that move people, not pixels. The work demands fluency with embedded systems security, secure ML pipelines, and the kind of operational paranoia that comes with public-facing autonomous fleets.
