Machine Learning Engineer Graduate (Global E-Commerce, Recommendation) - 2026 Start (PhD)

Responsibilities • Participate in building large-scale (10 million to 100 million) e-commerce recommendation algorithms and systems, including commodity recommendations, live stream recommendations, short video recommendations, and related components. • Build long- and short-term user interest models, analyze and extract information from large-scale data, and design algorithms to explore users' latent interests efficiently. • Design, develop, evaluate and iterate on predictive models for candidate generation and ranking (e.g., Click Through Rate and Conversion Rate prediction), including building real-time data pipelines, feature engineering, model optimization and innovation. • Design and build supporting/debugging tools as needed. • Support the production of scalable and optimized AI/machine learning (ML) models. • Focus on building algorithms for extraction, transformation and loading of large volumes of real-time, unstructured data to deploy AI/ML solutions from theoretical data science models. • Run experiments to test the performance of deployed models, and identify and resolve bugs that arise in the process. • Work in a team setting and apply knowledge in statistics, scripting and programming languages required by the firm. • Collaborate with relevant software platforms in which the models are deployed. Minimum Qualifications • Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline. • Strong programming and problem-solving ability. • Experience in applied machine learning, familiar with one or more of the algorithms such as Collaborative Filtering, Matrix Factorization, Factorization Machines, Word2vec, Logistic Regression, Gradient Boosting Trees, Deep Neural Networks, Wide & Deep, etc. • Experience in Deep Learning Tools such as TensorFlow/PyTorch. • Experience with at least one programming language like C++/Python or equivalent. Preferred Qualifications • Experience in recommendation systems, online advertising, information retrieval, natural language processing, machine learning, large-scale data mining, or related fields. • Publications at conferences such as KDD, NeurIPS, WWW, SIGIR, WSDM, ICML, IJCAI, AAAI, RECSYS and related venues. #J-18808-Ljbffr