Machine Learning Engineer Intern (E-Commerce Recommendation Live) - 2027 Start (PhD)

What you'll do

• Build and optimize models across recall, pre-ranking, ranking, and mixed ranking to improve business and user-value metrics.
• Develop cross-domain and multimodal recommendations connecting videos, live streams, products, and user behavior.
• Advance recommendation technologies, run experiments, and partner with cross-functional teams to launch scalable solutions.

What they ask for

• Currently pursuing a PhD in Computer Science, Engineering, Operations Research, or a related technical discipline.
• Solid machine learning foundation and experience in at least one area: recommendation systems, search, advertising, NLP, multimodal learning, or large-scale applied AI.
• Strong programming skills in Python or C++, with hands-on experience using deep learning frameworks such as PyTorch.
• Understanding of data structures, algorithms, and large-scale model training or production machine learning systems.

Nice to have

• Experience in recommendation systems or related large-scale consumer products; generative recommendation, large recommendation models, or retrieval and ranking systems.
• Experience with LLMs or multimodal foundation models, cross-domain learning, LTV modeling, long-sequence behavior modeling, or distributed training and inference optimization.

Tools

Python, C++, PyTorch

AI focus

Recommendation modeling for live commerce, including generative recommendation, large recommendation models, multimodal learning, reinforcement learning, and long-term value optimization.