Focus Multimodal Foundation Models · Representation Learning · Method Innovation We are looking for strong technical builders and researchers who deeply understand foundation models and representation learning beyond simply applying existing frameworks. Ideal candidates should have: • Strong experimental rigor • Solid systems and modeling intuition • Hands-on engineering ability • Interest in scalable multimodal AI systems for real-world autonomy We value people who can bridge research and production, and who care about robustness, scalability, efficiency, and practical deployment in large-scale autonomous driving systems. Responsibilities 1. Large-Scale Foundation Model Pretraining • Develop scalable pretraining pipelines for large-scale multimodal driving data • Design and optimize training strategies for: • Vision-language-action models • Video foundation models • Long-context temporal modeling • Multimodal representation alignment • Improve: • Training stability • Data efficiency • Scaling efficiency • Representation robustness • Work on distributed training systems and large-scale model optimization using frameworks such as: • PyTorch Distributed • DeepSpeed • Megatron-LM 2. Representation Learning & Method Innovation • Design and improve self-supervised and multimodal learning methods for real-world autonomous driving systems • Conduct architecture-level research on: • Vision Transformers (ViT) • Video / temporal architectures • Multimodal fusion and alignment • Embedding and retrieval systems • Long-context and memory-efficient architectures • Explore and improve: • Pretraining objectives • Loss functions • Training paradigms • Generalization and robustness • Analyze model behavior through: • Rigorous ablation studies • Failure case analysis • Representation probing and evaluation 3. Efficient Foundation Models & Scalable Deployment • Improve the efficiency, scalability, and deployability of large multimodal foundation models for real-world autonomous driving systems • Work on areas such as: • Model quantization • Knowledge distillation • Efficient attention mechanisms • Sparse architectures and Mixture-of-Experts (MoE) • Long-context and memory-efficient modeling • Inference acceleration and serving optimization • Training and inference system efficiency • Optimize model throughput, latency, memory usage, and deployment performance for large-scale production environments Requirements • MS or PhD in: • Computer Vision • Machine Learning • Robotics • Computer Science • Related fields • Strong understanding of: • Foundation models • Self-supervised learning • Representation learning • Multimodal learning • Large-scale pretraining • Hands-on experience with methods such as: • CLIP • DINO / DINOv2 • MAE • Contrastive learning • Masked modeling • MoE or scalable transformer architectures • Experience with one or more of the following is highly valued: • Video foundation models • Long-context modeling • Retrieval systems • Efficient inference • Distributed training • Model compression and deployment optimization • Strong publication record in top-tier venues is preferred: • CVPR • ICCV • ECCV • NeurIPS • ICLR • ICML