Ingénieur(e) “Staff" en apprentissage automatique — "BEV"

Master's or PhD in Computer Science, Electrical Engineering, Robotics, or related field (or equivalent practical experience). 10+ years of experience in deep learning for perception, 3D vision, or autonomous systems. Proven expertise in BEV modeling, 3D scene understanding, and multi-view fusion. Strong background in multimodal sensor fusion, particularly camera and LiDAR data 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. Proven leadership in driving ML model innovation and mentoring technical teams.

Lead the development of the BEV model, defining and executing the technical roadmap for BEV-based perception models across multiple tasks. Design advanced multimodal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations. Develop fundamental 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. Improve model robustness and generalization, accounting for long-tail conditions. Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance. Collaborate cross-functionally with sensor calibration, mapping, and fusion teams. Mentor and guide ML engineers, cultivating best practices. Stay at the forefront of ML research, exploring self-supervised learning, large-scale pre-training, or foundation models for 3D perception.