Data Scientist (LLM&Unsupervised Learning) - Applied AI

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.

About the Team
The success of TikTok's data business model hinges on the supply of a large volume of high quality labeled data that will grow exponentially as our business scales up. However, the current cost of data labeling is excessively high. The Data Solutions team is built to understand data strategically at scale for all Global Business Solution (GBS) business needs. Data Solutions Team uses quantitative and qualitative data to guide and uncover insights, turning our findings into real products to power exponential growth. Data Solutions Team responsibility includes infrastructure construction, recognition capabilities management, global labeling delivery management.

About the Role:
We are looking for an AI/ML technical expert, focusing on the application of multimodal LLMs, unsupervised learning, and clustering algorithms. The candidate will work closely with product, operations, policy, and engineering teams to leverage advanced natural language processing, computer vision, and deep learning technologies to solve business problems and extract data insights.

What You'll Do:
1. Leverage multimodal large language models, natural language processing, machine learning, or computer vision techniques to design and build core product capabilities, extract insights, and optimize monetization strategies;
2. Develop innovative algorithms and build prototypes for business problems using the latest deep learning, machine learning, statistical, and optimization techniques;
3. Use unsupervised learning and clustering algorithms to discover potential patterns and trends from large datasets and propose data-driven business solutions;
4. Collaborate with product managers and cross-functional teams to define user stories and success metrics, managing data projects from 0 to 1;
5. Use methods like AB testing to validate the business value and expected revenue of projects and continuously optimize model performance;
6. Work with engineering teams to deploy data models and scale solutions.