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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 Lane & Route Network 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.

• Lead the research, design, architecture, training and validation of advanced neural networks for vectorized mapping (e.g., MapTR), multi-camera BEV transformers, and multimodal fusion models to extract and model lane and route networks for both high-fidelity offline pipelines and real-time online mapping.
• Architect, design, and implement a production-grade lane and route network mapping stack, ensuring high-performance integration with upstream and downstream modules like Perception, Behavior, Policy, and Prediction.
• 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, specifically focusing on lane and route networks. 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, including online vectorized HD map construction, end-to-end mapping models, and vision/fusion Foundation Models to deliver production-ready solutions.

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:
• Vectorized mapping networks (e.g., MapTR), BEV-based scene representation, and temporal modeling.
• Cross-modal calibration and fusion (e.g., Camera-to-LiDAR) within Bird's-Eye-View (BEV) unified representation spaces.
• Transformers or Graph Neural Networks (GNNs) applied to structured lane geometry and topological connectivity.
• Lane-level topology and connectivity, intersection modeling, and lane/road network graph construction.
• Computer Vision Foundations: 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.
• Strong communication skills with the ability to lead technical discussions and align with cross-functional teams.

• 10+ years of experience in ML/DL for autonomous driving or ADAS systems.
• Experience with self-supervised and/or semi-supervised learning for large-scale representation learning.
• Experience utilizing Vision-Language Models (VLMs) and/or Foundation Models for auto-labeling and long-tail (edge-case) detection.
• Expertise in ML optimization for real-time products with limited compute, such as quantization, pruning, or distillation of large transformer models.
• A proven record of inventions and/or publication record at top-tier conferences (e.g., CVPR, NeurIPS, ICCV, ECCV, ICLR).

• Standard office working conditions which includes but is not limited to:
• Prolonged sitting
• Prolonged standing
• Prolonged computer use
• Travel required? -  Moderate: 11%-25%

Want to learn more about our culture & benefits? Check out our website!

May Mobility is an equal opportunity employer.  All applicants for employment will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity or expression, veteran status, genetics or any other legally protected basis.   Below, you have the opportunity to share your preferred gender pronouns, gender, ethnicity, and veteran status with May Mobility to help us identify areas of improvement in our hiring and recruitment processes. Completion of these questions is entirely voluntary.  Any information you choose to provide will be kept confidential, and will not impact the hiring decision in any way. If you believe that you will need any type of accommodation, please let us know.

Note to Recruitment Agencies: May Mobility does not accept unsolicited agency resumes. Furthermore, May Mobility does not pay placement fees for candidates submitted by any agency other than its approved partners.