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

Role overview A Staff Machine Learning Engineer position focused on Bird's-Eye View (BEV) and multi-modal perception for autonomous trucking. The role centers on designing and advancing perception models that fuse heterogeneous sensor data into rich spatial representations of the driving environment. It is a technical leadership track concentrated on model innovation and maturity rather than downstream feature integration.

Responsibilities - Define and execute the technical roadmap for BEV-based perception models spanning detection, segmentation, road topology, and scene understanding. - Architect multi-modal networks that unify camera, LiDAR, radar, and HD map inputs into cohesive spatial representations. - Build foundational perception models using BEV transformers, voxel-based encoders, or implicit scene representations. - Own large-scale training pipelines including data sampling, augmentation, distributed training, and hyperparameter optimization. - Improve model robustness and generalization across 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 with sensor calibration, mapping, and fusion teams to align perception model interfaces. - Mentor ML engineers on experimentation, code quality, and model validation practices. - Track relevant research, including self-supervised learning, large-scale pretraining, and 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 a related field, or equivalent practical experience. - Demonstrated expertise in BEV modeling, 3D scene understanding, and multi-view fusion. - Strong background in multi-modal sensor fusion, particularly camera and LiDAR integration. - Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow. - Experience with large-scale data pipelines, distributed training, and experiment management systems. - Track record of leading ML model innovation and mentoring technical teams.

Nice to have - Production experience in autonomous driving or robotics perception. - Familiarity with MLOps tooling and infrastructure such as Ray. - Hands-on expertise with BEV-based architectures, LiDAR-vision fusion, or spatial-temporal modeling. - Understanding of 3D labeling, calibration, and sensor simulation pipelines. - Publications or open-source contributions at top venues such as CVPR, ICCV, NeurIPS, ICRA, or CoRL. - Awareness of deployment constraints including latency, memory, and accuracy tradeoffs.

Benefits and work setup - Hybrid work available in Ann Arbor, MI, with remote options across the United States. - Compensation range of $215,500–$258,600 USD, plus bonus and stock options. - 100% employer-paid medical, dental, and vision premiums for full-time staff. - 401(k) plan with a 6% employer match. - Flexible scheduling, generous paid vacation available from the start date, and company-wide holiday closures. - AD+D and life insurance coverage.