Senior Machine Learning Engineer - Localization

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. 

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. 

• →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. 

• →Collaborate with perception, mapping, planning, controls, and platform teams to deliver integrated autonomous driving capabilities. 

• →Provide technical leadership through design reviews, code reviews, mentoring, and development of engineering best practices. 

 What You'll Need to Succeed 

• →Bachelor’s degree in Computer Science, Software Engineering, Robotics, or a related field with 6+ years of relevant industry experience, or a Master’s degree with 3+ years of relevant industry experience, or a PhD with 1+ year of relevant industry experience. 

• →Experience with AV or robotics localization systems (e.g., LiDAR-based localization, visual odometry, SLAM, or map-based pose estimation). 

• →Strong experience developing and deploying ML models for perception, localization, or sensor fusion domains.  

• →Proficiency with PyTorch and modern ML tooling for training, inference, and optimization.  

• →Solid understanding of 3D geometry, probabilistic estimation, coordinate transforms, and robotics fundamentals.  

• →Demonstrated ability to work with large multimodal datasets and build scalable pipelines for processing, labeling, and evaluation. 

• →Strong software engineering fundamentals in Python and C++, including algorithms, data structures, testing, debugging, and performance optimization. 

• →Strong written and verbal communication skills and the ability to work effectively on cross-functional teams. 

 Bonus Points 

• →Experience with state estimation techniques such as factor graphs, Kalman filtering, nonlinear optimization, or related probabilistic estimation methods. 

• →Familiarity with distributed computing tools such as Ray, Kubernetes, or similar orchestration frameworks.  

• →Knowledge of embedded and real-time constraints for on-vehicle deployment.  

• →Experience in simulation, synthetic data generation, and uncertainty-aware ML modeling.  

• →Contributions to open-source robotics, perception, or ML frameworks.  

• →Familiarity with functional safety standards and automotive development processes, including ISO 26262.