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Open nowPosted 121 days ago

Member of Technical Staff (MTS) - Multimodal Foundation Models

Workable (global search)107,990 open roles

Where
Fremont, CA, United States
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Your applicationOpen nowMember of Technical Staff (MTS) - Multimodal Foundation ModelsWorkable (global search) · Fremont, CA, United States

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7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 6 days.

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  2. 3.6%3 days
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  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 121 days ago

Workable (global search) median: 6 days open

The posting

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
  3. Machine Learning
  4. Robotics
  5. Computer Science
  6. Related fields
  7. Strong understanding of:
  8. Foundation models
  9. Self-supervised learning
  10. Representation learning
  11. Multimodal learning
  12. Large-scale pretraining
  13. Hands-on experience with methods such as:
  14. CLIP
  15. DINO / DINOv2
  16. MAE
  17. Contrastive learning
  18. Masked modeling
  19. MoE or scalable transformer architectures
  20. Experience with one or more of the following is highly valued:
  21. Video foundation models
  22. Long-context modeling
  23. Retrieval systems
  24. Efficient inference
  25. Distributed training
  26. Model compression and deployment optimization
  27. Strong publication record in top-tier venues is preferred:
  28. CVPR
  29. ICCV
  30. ECCV
  31. NeurIPS
  32. ICLR
  33. ICML
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