Torc is looking to develop machine-learning components for their autonomous trucks to precisely understand their location in the world, enabling robust, real-time localization in challenging environments.
Requirements
- 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 in 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, spatial transforms, and robotics fundamentals.
- Demonstrated ability to work with large multimodal datasets and build scalable pipelines for processing, labeling, and evaluation.
- Strong software engineering skills in Python or C++, with a focus on clean, maintainable, production-ready code.
- Familiarity with distributed computing tools such as Ray, Kubernetes, or similar orchestration frameworks.
Responsibilities
- Design, build, and optimize ML models for localization, including learned pose estimation, map-matching, and sensor fusion pipelines using camera, LiDAR, and radar data.
- Develop high-performance training and evaluation workflows, leveraging frameworks such as PyTorch, distributed training infrastructure, and large-scale datasets.
- Collaborate with robotics and mapping engineers to integrate localization models into the autonomy stack, ensuring performance, stability, and real-time constraints are met.
- Analyze failure cases, run ablations, improve model robustness, and drive rigorous experimentation to achieve production-level reliability.
- Contribute to system design, code reviews, best practices, and documentation across the ML and autonomy organization.
Other
- 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.
- Excellent communication skills and the ability to collaborate in a fast-paced, cross-functional environment.
- Knowledge of embedded and real-time constraints for on-vehicle deployment.
- Experience in simulation, synthetic data generation, and uncertainty-aware ML modeling.
