TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and its offices include New York, London, Dublin, Paris, Berlin, Dubai, 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.

Our Team Supply Side Algorithms
Our team is committed to expanding the number of merchants and creators on TikTok Shop, as well as providing them with comprehensive support to foster growth within the TikTok Shop ecosystem. We achieve this by developing end-to-end algorithmic capabilities utilizing machine learning, data mining, and causal inference methodologies.

We are seeking a talented and motivated Machine Learning Engineer with expertise in marketplace growth to join our dynamic and fast-paced team. In this role, you will collaborate with cross-functional teams including data scientists, product managers, and business stakeholders to develop innovative solutions that drive the growth of merchants and creators in TikTok Shop.

Responsibilities
1. Utilize advanced machine learning techniques to analyze large-scale datasets and identify meaningful, correlations, and causal relations related to merchant and creator growth in TikTok Shop
2. Collaborate with business stakeholders, product managers, and data scientists to define data mining objectives and develop strategies to address complex business problems and opportunities.
3. Apply feature engineering techniques to derive relevant features and embeddings from raw data and improve the performance of machine learning models.
4. Develop scalable and efficient data pipelines to preprocess and transform data for machine learning tasks, ensuring data quality, consistency, and availability.
5. Evaluate and benchmark different machine learning approaches, algorithms, and tools, and recommend the most appropriate solutions based on performance, scalability, and interpretability.
6. Stay updated with the latest advancements in data mining, machine learning, and related fields, and apply this knowledge to enhance the team's capabilities and identify new opportunities.
7. Communicate findings, insights, and technical concepts effectively to both technical and non-technical stakeholders, fostering a collaborative and data-driven decision-making culture.