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Salary: USD 177,300 - 212,800 / annual
Torc, now part of the Daimler family, is a leader in autonomous driving software for commercial trucks. The Tracking Team develops ML models, state estimation algorithms, and production software responsible for real-time tracking and kinematic estimation of other actors and objects. This is critical to safe autonomous vehicle operation.
In this role, you will design, develop, and deploy production ML models for multi-object tracking, including association, learned pose and kinematic estimation, and sensor fusion using camera, LiDAR, radar, and other vehicle sensors. You'll develop scalable training and evaluation workflows using PyTorch and distributed training infrastructure on large-scale real-world datasets. You'll design and improve tracking and sensor fusion algorithms for robust vehicle pose and kinematics estimation, and analyze large-scale vehicle data to characterize performance, identify failure modes, and drive improvements.
You'll develop robust, efficient production software in modern C++ and Python across the full development lifecycle, define evaluation, verification, and validation strategies to ensure tracking quality and safety across diverse operating conditions, and make technical design and architecture decisions balancing model performance, computational efficiency, robustness, and production constraints.
Collaboration is essential: you'll work with perception, mapping, planning, controls, and platform teams to deliver integrated autonomous driving capabilities. You'll provide technical leadership through design reviews, code reviews, mentoring, and development of engineering best practices.
REQUIREMENTS:
- Bachelor's degree in Computer Science, Software Engineering, Robotics, or related field with 6+ years of relevant industry experience; OR Master's degree with 3+ years of relevant industry experience; OR PhD with 1+ year of relevant industry experience.
- Experience with AV or robotics perception systems (tracking, detection, SLAM, scene modeling, etc.).
- 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 ability to work effectively on cross-functional teams.
BONUS:
- Experience with state estimation techniques (factor graphs, Kalman filtering, nonlinear optimization, or related probabilistic estimation methods).
- Familiarity with distributed computing tools (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.
