Machine Learning Engineer – BEV​/Multi-Modal Perception

• 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

• 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 Tensor Flow
• 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)

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.

• BEV Modeling
• 3D Scene Understanding
• Multi-Modal Sensor Fusion
• Deep Learning Frameworks (PyTorch, Tensor Flow)

• 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

• Mentoring
• Collaboration
• Experimentation
• Code Quality
• Model Validation Best Practices

• M.S. or Ph.D. in Computer Science
• Electrical Engineering
• Robotics

• Autonomous Systems
• Perception Models
• Sensor Calibration
• Mapping and Fusion
• Publications in CVPR, ICCV, NeurIPS, ICRA, CoRL

• Python
• Py Torch
• Tensor Flow
• Ray
• Experiment Management Systems