Member of Technical Staff (MTS) - Multimodal Foundation Models

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:
2. • • Computer Vision
     • Machine Learning
     • Robotics
     • Computer Science
     • Related fields
3. Strong understanding of:
4. • • Foundation models
     • Self-supervised learning
     • Representation learning
     • Multimodal learning
     • Large-scale pretraining
5. Hands-on experience with methods such as:
6. • • CLIP
     • DINO / DINOv2
     • MAE
     • Contrastive learning
     • Masked modeling
     • MoE or scalable transformer architectures
7. Experience with one or more of the following is highly valued:
8. • • Video foundation models
     • Long-context modeling
     • Retrieval systems
     • Efficient inference
     • Distributed training
     • Model compression and deployment optimization
9. Strong publication record in top-tier venues is preferred:
10. • • CVPR
      • ICCV
      • ECCV
      • NeurIPS
      • ICLR
      • ICML