Machine Learning Engineer (Recommendation) - Global E-Commerce

About the team

Our team works on large-scale recommendation systems for various offerings under ByteDance and its affiliates, focusing on developing recommendation algorithms/models/strategies. We are committed to developing cutting-edge solutions for e-commerce recommendation systems.

Responsibilities

• Work on recommendation systems, involving contents of various forms ranging from products, short videos to live streams, with each unified recommendation model fulfilling heterogeneous E-commerce scenarios/goals across multiple countries. - Optimize e-commerce recommendation models at massive scales, using deep learning/transfer learning/multi-task learning techniques. - Data mining and analysis to improve the quality of recommended contents. - Conduct research on various topics, which aim to optimize content recommendation circulation, ranging from ensuring diversity and new discovery in recommendation contents, to cold-start problem for new users/items and discovery of high-quality products/live streamers. - Develop innovative and state-of-the-art e-commerce models and algorithms - 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