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

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 models connecting videos, live streams, products, and user behavior.
• Partner with teams to launch scalable solutions, run experiments, and translate research into business impact.

What they ask for

• Currently pursuing an undergraduate degree in Software Development, Computer Science, Computer Engineering, or a related discipline.
• Solid machine-learning foundation and knowledge of at least one area: recommendation systems, search, advertising, NLP, multimodal learning, or large-scale applied AI.
• Strong Python or C++ skills and hands-on experience with 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, causal inference, or large-scale distributed training and inference optimization.

Tools

Python, C++, PyTorch

AI focus

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