Research engineer: Generative AI model personalization and efficient fine-tuning

Location: Seoul, Korea, Republic of

Department: Machine Learning Engineering

Work Location: onsite

Company:

Qualcomm Korea YH

Job Area:

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary:

We are making AI technology be around us at edge devices, attend and assist people and learn from interaction without many labels, together with other devices in a federated way. We are based in Seoul and work together with our other team members in San Diego, Amsterdam, and Beijing.

We are looking for deep-learning researchers interested in developing new theories and algorithms in the following areas, with a strong focus on Generative AI Models and its personalization:

• Generative foundation models: LLM, LVM, LMM, VLA, etc.
• Personalization and on-device adaptation of generative models
• Efficient fine-tuning techniques (LoRA, adapters, parameter-efficient tuning)
• Multi-modal generation (text-to-image, text-to-video, audio-visual)
• Diffusion / transformer models and their optimization
• Knowledge distillation and compression for generative models
• Quantization and low-bit processing for LLM/LMM
• 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 will 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

• Strong machine learning and deep learning knowledge
• Recent research experience in Generative AI (LLM, LVM, LMM, VLA) and personalization techniques
• Algorithm implementation experience using Python with deep learning platforms (e.g., PyTorch, TensorFlow)
• Publication requirement:
  • At least 4 papers as first author, including at least 2 papers in top conferences (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
• Proven expertise in 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

·       Strong track record of publications in top-tier conferences (e.g., NeurIPS, CVPR, ICML, ICCV, ICLR, ECCV, ACL)

Minimum Qualifications:

• 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.

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