Machine Learning Engineer Graduate (E-Commerce Recommendation Foundation)- 2027 Start (PhD)

What you'll do

• Participate in the pre-training, mid-training, and post-training lifecycle of Recommendation Foundation Models.
• Design and train multimodal semantic tokenizers that encode item content into discrete tokens.
• Develop LLM-native recommendation and systems spanning retrieval, ranking, and end-to-end generative recommendation.

What they ask for

• Completing or recently completed a PhD in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related discipline.
• Solid foundation in machine learning and deep learning, with strong interest in LLMs and generative recommendation.
• Proficiency in Python and experience with deep learning frameworks such as PyTorch.
• Self-driven, with a strong research mindset and solid engineering skills.

Nice to have

• Experience training LLMs or Foundation Models, or research/project experience in generative recommendation, LLM-native recommendation, or multimodal semantic tokenization.
• LLM-related publications at the listed top-tier conferences or strong achievements in major technical competitions.

Tools

Python, PyTorch, LLMs, VLMs

AI focus

Train Recommendation Foundation Models; develop multimodal semantic tokenizers and LLM-native recommendation, using multimodal understanding and generative recommendation approaches.