Staff Machine Learning Engineer – BEV/Multi-Modal Perception
jobtailorAnn Arbor (MI)
Staff Machine Learning Engineer – BEV/Multi-Modal Perception
Posted yesterday
jobtailorAnn Arbor (MI)
SENIORITY
Manager
About the role
Lead BEV model development and execute the technical roadmap for BEV-based perception models across detection, segmentation, road topology, and scene understanding Design multi-modal architectures that fuse camera, LiDAR, radar, and HD maps into unified spatial representations Develop foundational perception models using BEV transformers, voxel-based encoders, or implicit scene representations Own large-scale training workflows, including data sampling, augmentation, distributed training, and hyperparameter optimization Improve model robustness and generalization for 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 on cohesive perception model interfaces Mentor and guide ML engineers while cultivating experimentation, code quality, and model validation best practices Explore 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 systemsM.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 TensorFlowExperience 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)
Core Competencies:
Expertise in BEV model development and multi-modal sensor fusion, with a strong focus on deep learning for perception and 3D vision. Proven ability to lead technical teams, mentor engineers, and drive innovation in autonomous systems. Highest-signal resume keywordsBEV Modeling3D Scene Understanding Multi-Modal Sensor FusionDeep Learning Frameworks (PyTorch, TensorFlow) Hard SkillsDeep Learning for Perception3D VisionLarge-Scale Data Pipelines Distributed Training Hyperparameter Optimization Model Robustness Improvement Self-Supervised Learning Spatial-Temporal Modeling Camera and LiDAR Integration3D Labeling and Calibration Soft Skills MentoringCollaborationExperimentationCode Quality Model Validation Best Practices Certifications & QualificationsM.S. or Ph.D. in Computer Science Electrical Engineering RoboticsIndustry Keywords Autonomous Systems Perception ModelsSensor Calibration Mapping and Fusion Publications in CVPR, ICCV, NeurIPS, ICRA, CoRLTools & Technologies PythonPyTorchTensorFlowRayExperiment Management Systems
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