Member of Technical Staff (MTS) - Multimodal Foundation Models at Deeproute.ai

Fremont, California, United States -

Full Time

Start Date

Immediate

Expiry Date

26 Aug, 26

Salary

0.0

Posted On

28 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Multimodal Foundation Models, Representation Learning, PyTorch, DeepSpeed, Megatron-LM, Vision Transformers, Self-Supervised Learning, Model Quantization, Knowledge Distillation, Mixture-of-Experts, Distributed Training, Computer Vision

Industry

Software Development

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 MS or PhD in: Computer Vision Machine Learning Robotics Computer Science Related fields Strong understanding of: Foundation models Self-supervised learning Representation learning Multimodal learning Large-scale pretraining Hands-on experience with methods such as: CLIP DINO / DINOv2 MAE Contrastive learning Masked modeling MoE or scalable transformer architectures 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 Strong publication record in top-tier venues is preferred: CVPR ICCV ECCV NeurIPS ICLR ICML

Responsibilities

Develop and optimize scalable pretraining pipelines for multimodal foundation models used in autonomous driving. Conduct research on architecture-level innovations and improve model efficiency for real-world deployment.