Member of Technical Staff (MTS) - Multimodal Foundation Models

Responsibilities

• 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
• 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
• 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, or 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 such as CVPR, ICCV, ECCV, NeurIPS, ICML