Maymobility Lead ML Engineer at May Mobility to architect and implement production-grade mapping and localization systems for autonomous vehicles. Lead research and development of neural architectures for semantic and topological mapping.
Responsibilities
Architect, design, and implement production-grade lane and route network mapping stack
Lead research, design, training, and validation of advanced neural architectures
Drive feature development from inception to deployment including architecture design and code reviews
Own end-to-end data strategy for mapping domain including data curation and active learning pipelines
Develop metrics and evaluation frameworks for lane and route network accuracy and temporal consistency
Work independently with cross-functional teams to translate autonomy goals into software requirements
Collaborate with ML and Autonomy engineers for deployment and validation of mapping features
Qualification
PhComputer Vision FoundationsLane-level topology and connectivityVectorized mapping networks (eStrong understanding of HD maps
Required
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 temporal modeling.
Self-supervised/semi-supervised and vision/fusion Foundation Models.
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.