- Location
- Hanoi, Hanoi,VN, VN · Ho Chi Minh City, Ho Chi Minh City,VN, VN
- Workplace
- Remote
- Type
- Full-time
- Department
- Engineering
- Seniority
- Senior
- Education
- PhD
- Source
- Eightfold
Description
Design and train state-of-the-art foundation models for multi-modal applications. Develop algorithms for efficient inference and model compression to meet on-device constraints. Collaborate across hardware, software, and systems teams to bring research innovations to life on next-generation devices. Your work will directly impact smartphones, autonomous vehicles, robotics, and IoT systems worldwide. Foundation Model Fine-Tuning: Lead fine-tuning of large-scale LLMs and VLMs for multi-modal and agentic tasks. System Prototyping: Build and validate agentic AI prototypes for on-device and cloud deployment. Model Efficiency: Innovate quantization, pruning, and distillation strategies without sacrificing accuracy. Programming & Experimentation: Implement solutions in Python and PyTorch; manage large-scale experiments and benchmarks. Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. Ph.D. in Computer Science, Machine Learning, or related field (or equivalent experience). Proven experience in training large-scale LLMs/VLMs or foundation models. Strong background in deep learning, generative AI, and multi-modal architectures. Proficiency in Python and PyTorch; experience with distributed training frameworks. 3+ years of hands-on experience in developing and optimizing foundation models. Strong expertise in quantization, pruning, and techniques for efficient inference. Familiarity with hardware-aware AI optimization and deploying models on edge devices. Track record of publications in leading ML/AI conferences (e.g., NeurIPS, ICML, CVPR, ACL).
