Staff, Machine Learning Engineer - BEV/Multi-Modal Perception

Torc Robotics develops software for automated trucks and autonomous driving systems. The Staff Machine Learning Engineer will lead the development of BEV and multi-modal perception models that combine camera, LiDAR, radar, and HD map data for autonomous driving. The role focuses on model architecture, large-scale training, robustness, evaluation, technical leadership, and mentoring.

Responsibilities:

• 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 — from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization
• Advance model robustness and generalization, addressing long-tail conditions such as low visibility, occlusions, and 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, exploring self-supervised learning, large-scale pretraining, or foundation models for 3D perception

Requirements:

• 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 in driving ML model innovation and mentoring technical teams
• Experience with autonomous driving or robotics perception in production environments
• Experience with MLOps and infrastructure tools (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, accuracy)
• For this position, we are open to hiring in Ann Arbor, MI in a hybrid capacity
• We are also open to hiring Remote in the United States