Machine Learning Engineer Lead, Global E-Commerce Recommendation

TikTok is the leading destination for short-form mobile video. Our mission is to inspire creativity and bring joy. TikTok has global offices including Los Angeles, New York, London, Paris, Berlin, Dubai, Singapore, Jakarta, Seoul and Tokyo.

Why Join Us
Creation is the core of TikTok's purpose. Our platform is built to help imaginations thrive. This is doubly true of the teams that make TikTok possible.
Together, we inspire creativity and bring joy - a mission we all believe in and aim towards achieving every day.
To us, every challenge, no matter how difficult, is an opportunity; to learn, to innovate, and to grow as one team. Status quo? Never. Courage? Always.
At TikTok, we create together and grow together. That's how we drive impact - for ourselves, our company, and the communities we serve.
Join us.

About the team
Our team works on large-scale recommendation systems for various offerings under TikTok 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