Machine Learning Engineer Intern (Global E-Commerce, Recommendation) - 2027 Start (PhD)

About the Team E-commerce is a new and fast growing business that aims at connecting all customers to excellent sellers and quality products, through E-commerce live-streaming, E-commerce short videos, and commodity recommendation. We are a group of applied machine learning engineers and data scientists that focus on E-commerce recommendations. We are developing innovative algorithms and techniques to improve user engagement and satisfaction, converting creative ideas into business-impacting solutions. We are interested and excited in applying large scale machine learning to solve various real-world problems in E-commerce.

We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).

Responsibilities 1. Participate in building large-scale (billion level) E-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations etc. 2. Build long and short term user interest models, analyze and extract relevant information from large amounts of various data and design algorithms to explore users' latent interests efficiently. 3. Design, develop, evaluate and iterate on predictive models for candidate generation and ranking(eg. Click Through Rate and Conversion Rate prediction) , including, but not limited to building real-time data pipelines, feature engineering, model optimization and innovation. 4. Design and build supporting/debugging tools as needed. 5. Support the production of scalable and optimised AI/machine learning (ML) models. 6. 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. 7. Run experiments to test the performance of deployed models, and identify and resolve bugs that arise in the process. 8. Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm. 9. Work with the relevant software platforms in which the models are deployed.