Lead Machine Learning Engineer

Ann Arbor , MI - HQfull timeOn Site

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Lead Machine Learning Engineer at Maymobility · LumiJobs

senior

$225k–$275k

H1B: no

Maymobility The Lead Machine Learning Engineer at May Mobility leads the design and implementation of production-grade mapping and localization systems for autonomous vehicles. This role includes developing advanced neural architectures and managing data strategies for mapping domains.

Responsibilities

Architect, design, and implement a production-grade lane and route network mapping stack
Lead the research, design, training and validation of advanced neural architectures including object detection, classification, segmentation, tracking, depth estimation, and 3D reconstruction
Drive major feature development from inception to deployment including architecture design, code reviews, automated testing, mentorship, and technical resolution
Own the end-to-end data strategy for the mapping domain including data curation, auto-labeling, synthetic data, and active learning pipelines
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 seamless 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.