Lead ML Engineer, May Mobility

• 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
• Strong communication skills with the ability to lead technical discussions and align with cross-functional teams
• Don’t meet every single requirement? Studies have shown that women and/or people of color are less likely to apply to a job unless they meet every qualification. At May Mobility, we’re committed to building a diverse, inclusive, and authentic workforce, so if you’re excited about this role but your previous experience doesn’t align perfectly with every qualification, we encourage you to apply anyway! You may be the perfect candidate for this or another role at May

Desirable

• 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