Machine Learning Engineer Graduate (Global E-Commerce, Recommendation) - 2026 Start (BS/MS)
Core
Building large-scale e-commerce recommendation algorithms and systems to improve user engagement and conversion through live-streaming, short videos, and commodity recommendations.
Role type
Graduate Machine Learning Engineer (Recommendation Systems)
Builds
Large-scale recommendation systems for e-commerce live-streaming, short videos, and commodity discovery
Domain
E-commerce, Recommendation Systems, Large-scale Machine Learning
Deliverable
production ML models
Required skills
Applied machine learning, Deep Learning (TensorFlow/PyTorch), C++/Python, Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, Wide and Deep, Feature engineering, Real-time data pipelines, Model optimization
Preferred skills
Recommendation systems, Online advertising, Information retrieval, Natural language processing, Large-scale data mining, Academic publications (KDD, NeurIPS, etc.), Data mining competitions
Technologies
TensorFlow, PyTorch, C++, Python
Responsibilities
Design and develop predictive models for candidate generation and ranking (CTR/CVR), Build long and short term user interest models, Extract and transform large volumes of real-time unstructured data, Run experiments to test deployed model performance, Debug and resolve issues in the ML pipeline, Build supporting tools for model deployment
Seniority
Graduate (Entry-level)
