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, Mumbai, 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!
The E-Commerce Supply Chain and Logistics team is dedicated to enhancing clients' shopping experience and reducing operational costs in the supply chain and logistics of TikTok E-commerce by developing end-to-end algorithm capabilities using machine learning, operations research, data mining, and causal inference methods.

Role Overview:
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, engineers, product managers, and business stakeholders to develop innovative solutions that unlock the value hidden within our data, leading to improved decision-making and operational efficiencies.
Responsibilities:
1. Design and develop machine learning algorithms for various tasks, including but not limited to customer segmentation, scoring, outreach, marketing, characterization, and incentive optimization.
2. Support marketplace growth business goal attainment through optimizing outreach based on click through rate estimation, outreach timing selection, target audience selection and causal studies.
3. Collaborate with data scientists, analysts, and subject matter experts to define data mining objectives and develop strategies to address complex business problems and opportunities.
4. Apply feature engineering techniques to derive relevant features and embeddings from raw data and improve the performance of data mining models.
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.