Staff, ML Engineer - Road & Lane Detection

10+ years of experience developing deep learning models for perception or computer vision at scale. M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or related field (or equivalent experience). Deep expertise in semantic and instance segmentation, BEV modeling, or scene topology estimation. Strong understanding of lane and road geometry modeling, camera calibration, and sensor projection. Proficiency with Python and modern ML frameworks (e.g., PyTorch, Lightning). Experience with distributed training pipelines, experiment management, and large-scale dataset handling. Proven leadership in guiding technical roadmaps, mentoring engineers, and driving model improvements.

Own the model roadmap for Road & Lane Detection from concept to production. Research, design, and train advanced neural architectures for road and lane detection. Lead data strategy for the domain, including curation and labeling policies. Develop robust metrics and evaluation frameworks for road and lane geometry accuracy. Advance foundational capabilities like self-supervised pretraining and synthetic-to-real adaptation. Drive large-scale experiments for scalable improvements. Collaborate with other model development and perception teams. Mentor engineers and scientists on best practices. Evaluate and adapt emerging research techniques for production perception.