Senior Machine Learning Engineer - Localization

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 

Accurate and highly available information about vehicle motion and position is fundamental to the safe operation of autonomous trucks. Core capabilities such as perception, prediction, planning, and control depend on reliable ego-motion and localization estimates to ensure safe and efficient vehicle behavior. 

The Ego-Motion & Localization Team develops the ML models, state estimation algorithms, and production software responsible for estimating vehicle pose, velocity, and acceleration in real time. Our solutions fuse information from multiple sensors and maintain robust performance despite sensor imperfections, environmental challenges, and system degradation. As part of this team, you will develop and deploy production-ready localization and sensor fusion solutions and help solve the challenges of bringing autonomous vehicle technology to real-world commercial applications. 

What You'll Do 

• Design, develop, and deploy production ML models for ego-motion estimation and localization, including learned pose estimation, sensor extrinsic calibration, map matching, and sensor fusion using camera, LiDAR, radar, and other vehicle sensors. 

• Develop scalable training and evaluation workflows using PyTorch, distributed training infrastructure, and large-scale real-world datasets. 

• Design and improve state estimation and sensor fusion algorithms for robust vehicle pose, velocity, and acceleration estimation. 

• Analyze large-scale vehicle data to characterize performance, identify failure modes, and drive model and system improvements. 

• Develop robust, efficient production software in modern C++ and Python across the full development lifecycle. 

• Define evaluation, verification, and validation strategies to ensure localization quality, robustness, and safety across diverse operating conditions. 

• Make technical design and architecture decisions, balancing model performance, computational efficiency, robustness, and production constraints. 

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