Senior, ML Engineer - Road & Lane Detection

Bachelor's degree in Computer Science, Software Engineering, or related field with 6+ years of professional applied MLE engineering experience in Autonomous Vehicle, Robotics or related industry. Master's degree in Computer Science, Software Engineering, or related field with 3+ years of professional applied MLE engineering experience in Autonomous Vehicle, Robotics or related industry. Scientific understanding of machine learning for 3D BEV space modeling. Applied understanding and hands-on expertise in lane and road geometry concepts, multi-camera calibration, and sensor projection. Experience with understanding data distributions and analyzing long tail distributions. Mastery of Python and PyTorch.

Develop and optimize computer vision algorithms for road and lane detection. Train monocular and multimodal Road Model Detection models. Comprehend objects, lanes, obstacles, and weather conditions. Enhance perception systems for multi-modal sensor data. Utilize data science for model performance analysis. Design and implement deep learning models for BEV inference. Integrate BEV representations into planning and control pipelines. Develop efficient data processing and annotation pipelines. Implement data augmentation and synthetic data generation. Deploy ML models on edge devices for real-time performance. Optimize inference pipelines for automotive hardware. Collaborate with robotics, software, and hardware teams. Work with product and operations teams to define metrics. Stay updated with advancements in computer vision and autonomous driving. Translate scientific research into production pipelines. Contribute to model development roadmap and advise leadership. Mentor and guide junior team members.