Senior, ML Engineer - Road & Lane Detection

About the Company     At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.   A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight.    Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.   Meet the Team   Torc’s Model Development Organization is hiring a Senior ML engineer team who develops our next generation of Road-Lane BEV and image space models.   Torc's Autonomy Applications software utilizes cutting-edge deep learning techniques to perceive the vehicle's environment, predict the movements of other vehicles, and execute accurate driving decisions. We are actively seeking a highly experienced senior machine learning engineer to join our Road Lane perception team. This is an exceptional opportunity for you to have a significant impact on the future of the autonomous vehicle industry by leveraging AI.   As a Senior ML Engineer of the team, you are applying machine learning science in a production focused environment. You are using machine learning models in both a unimodal and multimodal context, to create a 3D representation of the road surface and lane geometry. Training, validation, data science, architectural design are your daily work. You are interested in understanding how your model performs in deployment, for what you collaborate closely with deployment focused teams. You mentor and guide more junior members of the team and are always interested in the newest trends in research, eager to translate scientific improvements into our production grade machine learning pipelines.   What You'll Do Develop and Optimize Computer Vision Algorithms   Training monocular and multimodal  Road Model Detection models.   Comprehending objects, lanes, obstacles, and weather conditions within the driving environment.   Enhance perception systems to process multi-modal sensor data (camera, LiDAR, radar) effectively.   Utilizing data science techniques to analyze model performance, data distributions, and identify corner cases.   Contribute to BEV Self-Driving Architectures   Design and implement deep learning models for Road Model inference in BEV frameworks.   Integrate BEV representations into end-to-end planning and control pipelines.   Use SD maps as priors for enhanced performance.   Data Management and Processing   Develop efficient pipelines for large-scale data processing and annotation(pseudo-labeling) of sensor data (e.g., LiDAR point clouds, image frames).   Implement data augmentation, synthetic data generation, and domain adaptation strategies to improve model robustness.   Model Deployment and Optimization   Deploy machine learning models on edge devices, ensuring real-time performance and resource efficiency.   Optimize inference pipelines for embedded and automotive-grade hardware platforms.   Cross-functional Collaboration   Collaborate with robotics, software, and hardware engineering teams to ensure seamless integration of perception systems.   Work with product and operations teams to define performance metrics and improve system reliability.   Research and Innovation   Stay updated with the latest advancements in computer vision, Road Lane monocular and BEV models, and autonomous driving technologies.   Translating scientific research into production-grade machine learning pipelines.   Publish findings in top-tier conferences and journals (optional but encouraged).   Leadership   Contributing to the model development roadmap and providing strategic advice to technical leadership.   Mentoring and guiding junior team members to enhance their technical skills and career growth.     What you’ll need to Succeed:   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, including the ability to apply state-of-the-art ML research and methods in production.   Applied understanding and hands-on expertise in lane and road geometry concepts, multi-camera calibration, and sensor projection.   Experience with understa