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

1. MS or PhD in:
• Computer Vision
• Machine Learning
• Robotics
• Computer Science
• Related fields
2. Strong understanding of:
• Foundation models
• Self-supervised learning
• Representation learning
• Multimodal learning
• Large-scale pretraining
3. Hands-on experience with methods such as:
• CLIP
• DINO / DINOv2
• MAE
• Contrastive learning
• Masked modeling
• MoE or scalable transformer architectures
4. 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
5. Strong publication record in top-tier venues is preferred:
• CVPR
• ICCV
• ECCV
• NeurIPS
• ICLR
• ICML