Member of Technical Staff; MTS - Multimodal Foundation Models

Job Title

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
• Deep Speed
• 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