Machine Learning Engineer Graduate (Global E-Commerce, Recommendation) - 2026 Start (PhD)
Core
Building large-scale e-commerce recommendation algorithms and systems for commodity, live stream, and short video recommendations to improve user engagement and conversion.
Role type
PhD-level 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 (TensorFlow/PyTorch), algorithm design (Collaborative Filtering, Matrix Factorization, Deep Neural Networks), feature engineering, model optimization, C++/Python programming, statistics
Preferred skills
Experience in recommendation systems, online advertising, information retrieval, NLP, large-scale data mining, publications at top ML conferences (KDD, NeurIPS, ICML, etc.)
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, extract and transform large volumes of unstructured data
Seniority
PhD, research-to-production IC
