# Member of Technical Staff (MTS) - Multimodal Foundation Models

**Company:** [Deeproute.ai](null/companies/f3G1qV4kLs8A4Na2Hrps4c.md)
**Location:** Fremont, United States
**Workplace:** on site

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## Description

**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

3.  Strong understanding of:

-   Foundation models
-   Self-supervised learning
-   Representation learning
-   Multimodal learning
-   Large-scale pretraining

5.  Hands-on experience with methods such as:

-   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:

-   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:

-   CVPR
-   ICCV
-   ECCV
-   NeurIPS
-   ICLR
-   ICML