Lead Machine Learning Engineer

Who you are

• 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 computer vision or localization at scale
• Deep expertise in several of the following areas:
• Computer Vision Foundations: Object detection, classification, segmentation, tracking, depth estimation, 3D reconstruction, and feature detection/description (e.g., SIFT, ORB, SuperPoint)
• Vectorized landmark and feature detection networks, BEV-based scene representation, and temporal modeling
• Self-supervised/semi-supervised learning, open-vocabulary detection, and vision/fusion Foundation Models
• Experience with feature extraction and/or fusion from imagery, LiDAR, and/or radar
• Expertise in ML/DL development using PyTorch or TensorFlow, including experience with synthetic data generation, large-scale dataset handling, data curation, and active learning strategies
• Strong programming skills in Python and/or C++ with experience in modular software design and Linux-based development
• Expertise in ML optimization for real-time products with limited compute, such as quantization and pruning of large transformer models
• Proven leadership in developing technical roadmaps, mentoring engineers, and driving measurable improvements in model performance and system reliability
• 10+ years of experience in ML/DL for autonomous driving or ADAS systems
• Experience utilizing Vision-Language Models (VLMs) and/or Foundation Models for auto-labeling and long-tail (edge-case) detection
• Working knowledge of localization and state estimation concepts (e.g., SLAM, sensor fusion)
• A proven record of inventions and/or publication record at top-tier conferences (e.g., CVPR, NeurIPS, ICCV, ECCV, ICLR)
• If you’re excited about this role but your previous experience doesn’t align perfectly with every qualification, we encourage you to apply anyway! You may be the perfect candidate for this or another role at May

What the job involves

• The Autonomy Mapping & Localization group builds the spatial intelligence, semantic and topological mapping, and state estimation that let our autonomous vehicles understand where they are and what the world looks like, and we scale that technology safely and reliably to commercial operations serving riders and consumers
• We are looking for a Lead ML Engineer to join our team and architect the next generation of our localization stack
• As the Lead ML Engineer for Localization, you will build the production-grade feature extraction and state estimation that lets our autonomous vehicles precisely navigate the world's most challenging roads at scale
• Architect and drive the technical roadmap for a production-grade localization machine learning stack, spanning map and sparse landmark-based localization (vision/LiDAR/radar), optimized for real-time performance, robustness against sensor degradation, and integration with the broader autonomy system across diverse Operational Design Domains (ODDs)
• 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 localization features (e.g., traffic signs, pole-like objects, keypoints, edges, signals, and road markings), for robust localization
• 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 localization feature extraction 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 localization performance, including feature extraction accuracy, temporal consistency, and system-level reliability across diverse ODDs
• Define and validate failure mode and degradation criteria for localization features across ODDs, ensuring safety case coverage and graceful fallback behavior under sensor or model failure
• Evaluate, adapt, and integrate frontier techniques, including multimodal localization and vision/fusion foundation models, translating research advances into production-ready solutions
• Drive cross-functional alignment, translating complex autonomy goals into clear software and system requirements
• Prolonged sitting
• Prolonged standing
• Prolonged computer use

Benefits

• Onsite, Hybrid, and Remote Work Options