Machine Learning Engineer (Recommendation), Global E-Commerce Alliance

About the team
The E-Commerce alliance team aims to serve merchants and creators on the e-commerce platform to meet merchants' business indicators and improve creators' creative efficiency. By cooperating with merchants and creators, we aim to provide high-quality content and a personalized shopping experience for our users, create efficient shopping tools at seller centers, and promote cooperation between merchants and creators.

Responsibilities

• Build and design large-scale machine learning algorithms to model the real-time interests of creators to improve conversion efficiency
• Optimize our massive commodity relationship mining and ranking and recall of creators' product recommendations, and improve the overall GMV of the platform
• Use the large scale of data to deeply analyze the behavior of creators and merchants and build targeted algorithm strategies to empower creators and merchants, improve merchants' monetization efficiency, and boost industrial upgrading
• Support the rapid development of our e-commerce business, serve millions of merchants, explore more efficient business models, satisfy users with the best shopping experience, and promote ecological development
• Support the production of scalable and optimised AI/machine learning (ML) models
• Focus on building algorithms for the extraction, transformation and loading of large volumes of realtime, unstructured data to deploy AI/ML solutions from theoretical data science models
• Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process
• Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm
• Work with the relevant software platforms in which the models are deployed

Minimum Qualifications:

• Bachelor's or Master's or PhD degree in Computer Science or related fields.
• Experience in C++ and Python. Proficiency in at least one Big Data tool (e.g., Hive SQL, Spark, MapReduce) and at least one Deep Learning tool (e.g., TensorFlow, PyTorch).
• Applied machine learning experience, familiarity with algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, etc.
• Industry experience in personalized recommendation, online advertising, information retrieval, or related fields.

Preferred Qualifications:

• Excellent learning ability and good communication and cooperative spirit.
• Passionate about techniques and solving challenging problems.
• Publications at KDD, NeurIPS, WWW, SIGIR, WSDM, CIKM, ICLR, ICML, IJCAI, AAAI, RecSys, and related conferences.