• Lead BEV model development: Define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding). • Design advanced multi-modal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations. • Develop foundational 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. • Advance model robustness and generalization, addressing 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 cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces. • Mentor and guide ML engineers, cultivating best practices in experimentation, code quality, and model validation. • Stay at the forefront of ML research, exploring self-supervised learning, large-scale pretraining, or foundation models for 3D perception.