Member of Technical Staff (MTS) - Multimodal Foundation Models
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Before you apply
SalaryNo pay range posted
LocationFremont, California, United States
SeniorityLead
Company size4+ people
PostedJun 6, 2026 · 5mo ago
Verified live2d ago
May no longer be actively hiring
Posted by the company on Jun 6, 2026No pay range listed, though California requires one
At a glanceSummarised by Seekless from the posting.
Strong experimental rigor
Solid systems and modeling intuition
Hands-on engineering ability
Interest in scalable multimodal AI systems for real-world autonomy
MS or PhD in Computer Vision, Machine Learning, Robotics, Computer Science or related fields
Strong understanding of foundation models, self-supervised learning, representation learning, multimodal learning, large-scale pretraining
Hands-on experience with CLIP, DINO/DINOv2, MAE, contrastive learning, masked modeling, MoE or scalable transformer architectures
Experience with video foundation models, long-context modeling, retrieval systems, efficient inference, distributed training, model compression and deployment optimization
Strong publication record in top-tier venues (CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML)
Skills
PyTorch Distributed
DeepSpeed
Megatron-LM
Vision Transformers
CLIP
DINO
DINOv2
MAE
MoE
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
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Develop scalable pretraining pipelines for large-scale multimodal driving data
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Design and optimize training strategies for:
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Vision-language-action models
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Video foundation models
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Long-context temporal modeling
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Multimodal representation alignment
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Improve:
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Training stability
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Data efficiency
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Scaling efficiency
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Representation robustness
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Work on distributed training systems and large-scale model optimization using frameworks such as:
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PyTorch Distributed
•
DeepSpeed
•
Megatron-LM
2. Representation Learning & Method Innovation
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Design and improve self-supervised and multimodal learning methods for real-world autonomous driving systems
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Conduct architecture-level research on:
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Vision Transformers (ViT)
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Video / temporal architectures
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Multimodal fusion and alignment
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Embedding and retrieval systems
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Long-context and memory-efficient architectures
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Explore and improve:
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Pretraining objectives
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Loss functions
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Training paradigms
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Generalization and robustness
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Analyze model behavior through:
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Rigorous ablation studies
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Failure case analysis
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Representation probing and evaluation
3. Efficient Foundation Models & Scalable Deployment
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Improve the efficiency, scalability, and deployability of large multimodal foundation models for real-world autonomous driving systems
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Work on areas such as:
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Model quantization
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Knowledge distillation
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Efficient attention mechanisms
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Sparse architectures and Mixture-of-Experts (MoE)
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Long-context and memory-efficient modeling
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Inference acceleration and serving optimization
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Training and inference system efficiency
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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