Staff, Machine Learning Engineer - BEV/Multi-Modal Perception
Torcrobotics · Remote, U.S, Ann Arbor, MI
About this role
## Staff, Machine Learning Engineer - BEV/Multi-Modal Perception ### About the Company At Torc, we believe autonomous vehicle technology will transform how we travel, move freight, and do business. Torc has been a leader in autonomous driving since 2007 and has spent over a decade commercializing solutions with experienced partners. Now part of the Daimler family, Torc focuses solely on developing software for automated trucks to transform how the world moves freight. ### Meet the Team As a **Staff Machine Learning Engineer** specializing in **BEV (Bird’s-Eye View)** and **Multi-Modal Perception**, you will lead development of next-generation models that unify information across **cameras, LiDAR, and radar** to deliver rich spatial understanding of the driving environment. This is a technical leadership role focused on **model innovation and maturity** (not downstream feature integration). ### What You’ll Do - Lead **BEV model development**: define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding). - Design advanced **multi-modal architectures** that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations. - Develop foundational perception models leveraging **BEV transformers**, voxel-based encoders, or implicit scene representations. - Own **large-scale training workflows**: data sampling strategies, augmentation pipelines, distributed training, and hyperparameter optimization. - Advance **model robustness and generalization** for long-tail conditions (low visibility, occlusions, rare scene configurations). - Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance. - Collaborate cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces. - Mentor and guide ML engineers, cultivating best practices in experimentation, code quality, and model validation. - Stay at the forefront of ML research (e.g., self-supervised learning, large-scale pretraining, foundation models for 3D perception). ### What You’ll Need to Succeed - **10+ years** of experience in deep learning for perception, 3D vision, and/or autonomous systems. - **M.S. or Ph.D.** in Computer Science, Electrical Engineering, Robotics, or related field (or equivalent practical experience). - Proven expertise in **BEV modeling**, **3D scene understanding**, and **multi-view fusion**. - Strong background in **multi-modal sensor fusion**, particularly integrating camera and LiDAR data. - Proficiency in **Python** and deep learning frameworks such as **PyTorch** or **TensorFlow**. - Experience with **large-scale data pipelines**, **distributed training**, and experiment management systems. - Demonstrated leadership driving ML model innovation and mentoring technical teams. ### Bonus Points - Experience with autonomous driving or robotics perception in production environments. - Experience with **MLOps** and infrastructure tools (e.g., Ray). - Hands-on expertise in BEV-based ML architectures, LiDAR-vision fusion, or spatial-temporal modeling. - Familiarity with 3D labeling, calibration, and sensor simulation pipelines. - Track record of publications or open-source contributions in top-tier venues (CVPR, ICCV, NeurIPS, ICRA, CoRL). - Understanding of performance tradeoffs and deployment constraints (latency, memory, acc
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