Lead ML Engineer - Mapping

About the role

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.

The Autonomy Mapping & Localization group's mission is to provide our vehicles with world-class spatial intelligence, semantic and topological mapping, and state estimation to model and navigate complex urban, suburban, and rural environments. We are looking for a Lead ML Engineer to join our team and architect the next generation of our mapping and localization stack. As the Lead ML Engineer for Mapping, you will be at the forefront of this mission, building the production-grade semantic and topological foundation that allows our vehicles to understand and navigate the world's most challenging roads at scale.

Key facts

What you'll do

Architect, design, and implement a production-grade lane and route network mapping stack, ensuring high-performance integration with the broader autonomy system.

Lead the research, design, training and validation of advanced neural architectures. This includes object detection, classification, segmentation, tracking, depth estimation, and 3D reconstruction to extract and model lane and route networks, alongside key semantic features (e.g., traffic signs, signals, and road markings), for automated mapping.

Drive major feature development from inception to deployment. This includes high-level architecture design, rigorous code reviews, automated testing, mentorship of junior engineers, and technical resolution.

Own the end-to-end data strategy for the mapping domain. You will define data curation, auto-labeling, synthetic data, and active learning pipelines to capture and resolve long-tail scenarios.

Develop robust metrics and evaluation frameworks for lane and route network accuracy, temporal consistency, and scaling across diverse Operational Design Domains (ODDs).

Work independently with cross-functional teams to translate complex autonomy goals into clear software and system requirements.

Collaborate with ML and Autonomy engineers to ensure the seamless deployment and validation of mapping features to the vehicle fleet.

Stay at the research frontier by evaluating, adapting, and innovating cutting-edge techniques. This includes online vectorized HD map construction, end-to-end mapping models, and vision/fusion foundation models to deliver production-ready solutions.

Establish scalable monitoring and debugging tooling for mapping models in production to support rapid iteration and high reliability.

Champion best practices for reproducibility, experiment tracking, and model versioning across the mapping team.

Define and improve data schemas, annotation guidelines, and quality gates to ensure consistency across geographic regions and operational conditions.

Partner with product and safety teams to align mapping outputs with functional requirements and regulatory constraints.

Explore novel representations such as topological graphs and vectorized lanes to improve robustness and generalization in unseen environments.

Contribute to the broader technical community through internal reviews, documentation, and knowledge transfer sessions.

Requirements

Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:

Ph.D. or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field with a strong mathematical and engineering foundation.

7+ years of industry experience developing and deploying ML/DL models for mapping or computer vision at scale.

Deep expertise in several of the following areas: Computer Vision Foundations: Object detection, classification, segmentation, tracking, depth estimation, and 3D reconstruction. Lane-level topology and connectivity, intersection modeling, and lane/road network graph construction. Vectorized mapping networks (e.g., MapTR), BEV-based scene representation, and camera-based localization. Strong proficiency in modern deep learning frameworks and production ML pipelines. Experience with large-scale data pipelines, distributed training, and model deployment in cloud and edge environments. Demonstrated ability to turn ambiguous problems into well-defined technical tasks and execute against tight deadlines. Excellent communication skills to collaborate with cross-functional stakeholders and translate technical concepts to non-technical audiences. A proven track record of delivering robust software systems that improve with real-world data and fleet feedback. Commitment to safety-critical thinking and rigorous validation required for autonomy applications.

Practical notes

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