Machine Learning Engineer Graduate (Global E-Commerce, Recommendation) - 2027 Start
Core
Building large-scale E-commerce recommendation algorithms and systems for commodity, live stream, and short video recommendations to improve user engagement.
Role type
Graduate Machine Learning Engineer (Recommendation Systems)
Builds
Large-scale recommendation algorithms and real-time data pipelines for E-commerce platforms
Domain
E-commerce, Recommendation Systems, Applied Machine Learning
Deliverable
production ML models
Required skills
Applied machine learning, Deep Learning (TensorFlow/PyTorch), Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, Wide and Deep, C++/Python, Feature engineering, Model optimization
Preferred skills
Recommendation systems, Online advertising, Information retrieval, NLP, Large-scale data mining, Academic publications (KDD, NeurIPS, etc.), Kaggle/KDD-cup experience
Technologies
TensorFlow, PyTorch, C++, Python
Responsibilities
Design and develop predictive models for candidate generation and ranking, Build user interest models, Run experiments to test deployed model performance, Debug and resolve issues in ML pipelines, Work with software platforms for model deployment
Seniority
Graduate (Entry-level)
