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

What you'll do

• Build and optimize recommendation models to improve GMV, conversion, watch time, and long-term user value.
• Develop cross-domain and multimodal models connecting videos, live streams, products, and user behavior; advance generative recommendation, large recommendation models, and reinforcement learning.
• Collaborate across teams to launch scalable solutions, run experiments, and translate research into measurable business impact.

What they ask for

• Currently pursuing a PhD in Computer Science, Engineering, Operations Research, or a related technical discipline.
• Strong machine-learning foundation and experience in at least one of 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, particularly live commerce, e-commerce, search, ads, or other large-scale consumer products.
• Experience with generative recommendation, large recommendation models, LLMs, multimodal models, or related recommendation and optimization methods.

Tools

Python, C++, PyTorch

AI focus

Develop and optimize live-commerce recommendation models, including generative recommendation, large recommendation models, reinforcement learning, multimodal learning, and long-term value optimization.