Lead ML Engineer - Lane & Route Networking Mapping

Essential Responsibilities

• 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.

Qualifications and Experience

Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:

Required

• 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 skill