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

What you'll do

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

What they ask for

• Completing or recently completed a Bachelor’s degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related discipline.
• Solid machine learning foundation and knowledge of 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.
• Good 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; familiarity with generative recommendation, retrieval and ranking, or recommendation architecture upgrades.
• Experience with LLMs or multimodal models, cross-domain transfer learning, LTV modeling, long-term value optimization, causal inference, debiasing, or distributed training and inference optimization.

Tools

Python, C++, PyTorch, LLMs

AI focus

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