Lead ML Engineer - Mapping

• →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, training and validation of advanced neural architectures. This includes object detection, classification, segmentation, tracking, depth estimation, and 3D reconstruction to extract and model lane and route networks, alongside key semantic features (e.g., traffic signs, signals, and road markings), for automated mapping.
• →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. 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.

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.
• Strong communication skills with the ability to lead technical discussions and align with cross-functional teams.

• 10+ years of experience in ML/DL for autonomous driving or ADAS systems.
• Experience with feature extraction and/or fusion from both street-level and overhead imagery.
• Experience utilizing Vision-Language Models (VLMs) and/or Foundation Models for auto-labeling and long-tail (edge-case) detection.
• Expertise in ML optimization for real-time products with limited compute, such as quantization and pruning of large transformer models.
• A proven record of inventions and/or publication record at top-tier conferences (e.g., CVPR, NeurIPS, ICCV, ECCV, ICLR).

• Standard office working conditions which includes but is not limited to:
• Prolonged sitting
• Prolonged standing
• Prolonged computer use
• Travel required? -  Moderate: 11%-25%