Machine Learning Engineer Graduate (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 to improve user engagement.
Role type
PhD-level applied machine learning engineer (recommendation systems)
Builds
Production ML models for candidate generation and ranking in E-commerce
Domain
E-commerce, large-scale machine learning, recommendation systems
Deliverable
production ML models
Required skills
Deep learning (TensorFlow/PyTorch), algorithm design (Collaborative Filtering, Matrix Factorization, Deep Neural Networks), real-time data pipeline construction, feature engineering, C++/Python programming
Preferred skills
Experience in information retrieval, NLP, online advertising, publications at top ML conferences (KDD, NeurIPS, etc.), Kaggle competition experience
Technologies
TensorFlow, PyTorch, C++, Python
Responsibilities
Design and develop predictive models for CTR and conversion rate prediction, build real-time data pipelines, run experiments to test deployed model performance, debug production systems, extract and transform large volumes of unstructured data
Seniority
PhD, research-to-production IC
