Reinforcement Learning for Generative AI (RLHF, policy optimization, reward modeling)
Unsupervised / semi-supervised / self-supervised learning
Meta-Learning / few-shot learning / domain adaptation / knowledge transfer
The developed technologies can be deployed on mobile device applications and have a worldwide impact on on-device learning algorithms using Qualcomm chips. Also, the research results are expected to be published in top-tier conferences and journals.
Minimum Qualifications
Currently pursuing a Ph.D. or Master's degree in Electrical Engineering, Computer Science, Artificial Intelligence, or a related field.
Strong knowledge of machine learning and deep learning
Research experience in Generative AI (LLM, LVM, LMM, VLA) and/or personalization techniques
Experience implementing ML algorithms using Python and deep learning frameworks (e.g., PyTorch, TensorFlow)
Strong research and problem-solving skills demonstrated through publications or research projects.
Publication requirement
At least 2 first-author papers, including at least 1 paper in a top-tier conference (e.g., NeurIPS, CVPR, ICML, ICCV, ICLR, ECCV, ACL)
Preferred Qualifications
Extensive and diverse experience with generative AI models, including LLMs, LVMs, LMMs, and multi-modal architectures
Familiarity with efficient adaptation techniques such as LoRA, adapters, and other parameter-efficient fine-tuning methods
Hands-on experience with model optimization for edge deployment, including quantization, pruning, and low-bit inference/training
Track record of publications at leading AI conferences (e.g., NeurIPS, CVPR, ICML, ICCV, ICLR, ECCV, ACL)
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