Maymobility Lead Machine Learning Engineer designs, trains, and evaluates state-of-the-art models for autonomous driving and simulation. Leads small teams and defines data and training practices for large-scale ML applications.
Responsibilities
Design, train and evaluate models for autonomous driving and simulation
Leverage emerging techniques in End-to-End driving, VLA, World or Foundation models
Lead small teams of cross-functional engineers
Define data balance, training experiment and evaluation practices at petabyte scale
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.