Research Scientist - Multimodal Representation Learning

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 Qualifications 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 #J-18808-Ljbffr