Lead ML/Perception Engineer

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May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think. Our vehicles do more than just drive themselves - they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We're building the world's best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces. Since our founding in 2017, we've given more than 500,000 autonomous rides to real people around the globe. And we're just getting started. We're hiring people who share our passion for building the future, today, solving real-world problems and seeing the impact of their work. Join us. Job Summary May Mobility is entering an exciting phase of growth as we expand our first-of-its-kind autonomous shuttle and mobility services across the nation. Launched in 2017 with a strong team of experienced roboticists and software engineers with decades of experience fielding robotic systems in the wild, May Mobility is looking to expand its team of robotics engineers with a background in robotics or autonomous vehicles. As Perception Lead, you will own the technical direction of how our vehicles see and interpret the world. This is a hands-on role focused on three priorities: advancing scene detection so the system reliably recognizes its operating context and engages the right behavior; hardening perception against sensor degradation and adverse weather (rain, snow, fog, glare, low light, and sensor soiling or occlusion) so performance degrades gracefully and safely; and driving major architectural updates to evolve our perception stack into a scalable, maintainable, production-grade foundation. You will set the technical bar, mentor engineers, and deliver capabilities that operate safely in the real world. Essential Responsibilities Work independently with cross-functional teams to develop software and system requirements. Lead major architectural updates to the perception stack, including system-level design, modularization, and migration strategies that improve scalability, latency, and maintainability across the fleet. Design, develop, and own scene detection and activation capabilities, classifying the operating scene/scenario and triggering the appropriate perception behaviors and operational design domain (ODD) logic at runtime. Improve perception robustness under degradation and adverse weather, including sensor fault detection, health monitoring, graceful degradation, and fallback strategies for rain, snow, fog, glare, low-light, and sensor soiling/occlusion conditions. Design, implement, and test state-of-the-art perception features on time with high quality, industrial-grade production code. Integrate large-scale multi-modal models (including but not limited to VLMs and LLMs) into the perception stack to improve semantic scene understanding and reasoning. Track and trend technical performance of perception in the field. Lead major feature development including feature design, code reviews, issue diagnosis, and resolution. Lead extensive testing to validate features and satisfy release schedules. Lead development related to data, development, and ML pipelines, specifically focused on multimodal data alignment for training foundation models. Skills and Abilities Success in this role typically requires the following competencies: Proven ability to architect and evolve large-scale perception or autonomy systems, balancing performance, latency, safety, and long-term maintainability. Strong grasp of perception robustness and reliability engineering, including degraded-sensor operation, adverse-weather handling, fault detection, and graceful degradation. Experience with scene/scenario understanding and runtime activation logic (e.g., context classification, ODD monitoring, behavior triggering). Familiar with ML development cycle, deployment, and optimization. Deep understanding of data: data pipeline, data balancing, data mining, and data-driven performance improvement. Knowledge of multimodal learning techniques, including contrastive learning and prompt engineering for zero-shot visual recognition. Deep understanding of testing frameworks and workflows. Excellent attention to detail and rigorous testing methodology. Exceptional written and verbal communication skills and team-leading abilities. Qualifications and Experience Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience: Required A minimum of 5+ years of industry experience working on real-world robot systems maintaining high-quality industrial-grade code. Master

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