About this opportunity
Jobtailor lists this Staff Machine Learning Engineer – BEV/Multi-Modal Perception opportunity in ann arbor, Michigan. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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 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)
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 keywords
BEV Modeling
3D Scene Understanding
Multi-Modal Sensor Fusion
Deep Learning Frameworks (PyTorch, TensorFlow)
Hard Skills
Deep Learning for Perception
3D Vision
Large-Scale Data Pipelines
Distributed Training
Hyperparameter Optimization
Model Robustness Improvement
Self-Supervised Learning
Spatial-Temporal Modeling
Camera and LiDAR Integration
3D Labeling and Calibration
Soft Skills
Mentoring
Collaboration
Experimentation
Code Quality
Model Validation Best Practices
Certifications & Qualifications
M.S. or Ph.D. in Computer Science
Electrical Engineering
Robotics
Industry Keywords
Autonomous Systems
Perception Models
Sensor Calibration
Mapping and Fusion
Publications in CVPR, ICCV, NeurIPS, ICRA, CoRL
Tools & Technologies
Python
PyTorch
TensorFlow
Ray
Experiment Management Systems
#J-18808-Ljbffr
Worksite address
ann arbor, MI, 48113, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.