Lead ML Engineer - Mapping
Confirmed live in the last 24 hours
May Mobility
Compensation
$225,000 - $275,000/year
Job Description
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.
Essential Responsibilities
- 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, and training of advanced neural architectures. This includes vectorized mapping networks (e.g., MapTR), multi-camera BEV transformers, and LiDAR-camera fusion models to extract and model lane and route networks for offline and online mapping.
- Lead major feature development from inception to deployment. This includes high-level architecture design, rigorous code reviews, automated testing, 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.
Qualifications and Experience
Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:
Required
- 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:
- Vectorized mapping networks (e.g., MapTR), BEV-based scene representation, and temporal modeling.
- Self-supervised learning and vision/fusion foundation models.
- Multimodal sensor fusion (Camera, LiDAR, radar, GPS/IMU).
- Lane-level topology and connectivity, intersection modeling, and lane/road network graph construction.
- Computer Vision tasks: Object detection, classification, segmentation, tracking, depth estimation, and 3D reconstruction.
- Strong understanding of HD maps, including lane and road network geometry modeling, connectivity, and semantic attributes.
- Expertise in ML/DL development using PyTorch or TensorFlow, including experience with distributed training, synthetic data generation, large-scale dataset handling, and data curation strategies.
- Strong programming skills in Python and/or C++ with experience in modular software design and Linux-based development.
- Proven leadership in guiding technical roadmaps, mentoring engineers, and driving measurable improvements in model performance and system reliability.
