Machine Learning Engineer Intern (Global E-Commerce, Recommendation) - 2027 Start (PhD)
Core
Building large-scale E-commerce recommendation algorithms and systems for commodity, live stream, and short video recommendations.
Role type
PhD intern, applied machine learning engineer (recommendation systems)
Builds
Large-scale recommendation algorithms and real-time data pipelines for E-commerce platforms
Domain
E-commerce, large-scale machine learning, recommendation systems
Deliverable
production ML models
Required skills
Deep learning frameworks (TensorFlow/PyTorch), algorithm design (Collaborative Filtering, Matrix Factorization, Deep Neural Networks), C++/Python programming, statistics, feature engineering, model optimization
Preferred skills
Experience in recommendation systems, NLP, information retrieval, publications at top ML conferences (KDD, NeurIPS, etc.), Kaggle competition experience
Technologies
TensorFlow, PyTorch, C++, Python
Responsibilities
Design and develop predictive models for candidate generation and ranking, build real-time data pipelines, run experiments to test deployed model performance, debug production issues, work with software platforms for model deployment
Seniority
Intern, research & development
