Machine Learning Engineer Graduate (Global E-Commerce, Recommendation Alliance) - 2026 Start (BS/MS)
Core
Build and design large-scale machine learning algorithms to model real-time interests of creators and optimize product recommendations to improve conversion efficiency and GMV for e-commerce merchants and creators.
Role type
Machine Learning Engineer (Recommendation Systems)
Builds
Scalable AI/ML models for real-time interest modeling, commodity relationship mining, ranking, and recall.
Domain
E-commerce, Recommendation Systems
Deliverable
production ML models
Required skills
Machine learning theory, Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, model training and deployment, PyTorch, TensorFlow, MXNet, large-scale data analysis, ETL for unstructured data
Preferred skills
Publications at top ML conferences (KDD, NeurIPS, WWW, SIGIR, WSDM, CIKM, ICLR, ICML, IJCAI, AAAI, RecSys), internship or project experience in e-commerce, recommendations, or search engines
Technologies
PyTorch, TensorFlow, MXNet
Responsibilities
Build algorithms for extraction, transformation, and loading of large volumes of real-time unstructured data to deploy AI/ML solutions; Support the rapid development of e-commerce business by exploring efficient business models; Support the production of scalable and optimized AI/ML models.
Seniority
Graduate (Entry-level)
