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

What you'll do

• Improve retrieval, ranking, re-ranking, and multi-queue blending models across the recommendation funnel.
• Develop user-interest and multimodal representations, and improve recommendation diversity, exploration, and cold-start performance.
• Investigate open recommendation problems, including long-term value, retention, attribution, fatigue, and LLM4Rec.

What they ask for

• Completing or recently completed a bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related discipline.
• Strong algorithms, data structures, coding, machine-learning, probability, and statistics foundations.
• Fluent in PyTorch or TensorFlow and at least one of Spark, Flink, or SQL; proficient in Python, with working C++ and Linux skills.
• Ability to analyze metrics and apply experimental rigor, including A/B test design and statistical confidence.

Nice to have

• Research or engineering experience in recommendation, search, ads, information retrieval, NLP, or large-scale ML systems; publications, open-source contributions, or relevant competition awards.
• Hands-on experience with causal inference, online learning or bandits, graph neural networks, sequence modelling, or large-scale distributed training; experience using AI coding and agentic workflows.

Tools

PyTorch, TensorFlow, Spark, Flink, SQL, Python, C++, Linux

AI focus

Build and optimize machine-learning recommendation systems, including multimodal representation learning, user-interest modelling, and exploration; investigate LLM4Rec.