Senior Machine Learning Engineer - TikTok Content Ecology

TikTok Content Ecology Algorithm Team leverages multimodal, LLM/MLLM, NLP & CV, and recommendation technologies to provide platform-level algorithmic capabilities for various business lines, including local services, search, main architecture, PGC (Professionally Generated Content), and UG (User Growth).

Our work spans short video content understanding, global trending event detection, intelligent customer service, and inspiration recommendation.

There are over a hundred languages here, presenting multilingual challenges for NLP/LLM technologies.

With a vast amount of short video content, MLLM and multimodal technologies have more application scenarios. The platform has a large user base, with hundreds of millions of creators, providing rich application scenarios for inspiration recommendation technologies.

All business lines are in a period of rapid growth, with overall DAU and revenue increasing rapidly, offering vast development opportunities.

Welcome to join us！



About the Team

The Content Ecology Algorithm Team drives TikTok’s AI innovations in LLMs, NLP, Computer Vision (CV), multimodal learning, and recommendation algorithms. We develop cutting-edge AI capabilities that power multiple business lines, including Local Services, Search, Core Video Architecture, Professional Content (PGC), and User Growth (UG).

Our work includes:

- Short Video Content Understanding – Building multimodal AI models to analyze video, text, and speech.

- Global Trending Event Detection – Developing real-time AI systems to detect viral trends worldwide.

- Intelligent Customer Service – Implementing chatbot and automation solutions using LLMs.

- AI-Driven Content Discovery – Enhancing personalized content recommendations through advanced algorithms.

With millions of daily users, our work directly impacts TikTok’s growth and user engagement.



Responsibilities

- Develop and optimize LLM, NLP, CV, and recommendation models to improve TikTok’s content ecosystem.

- Implement multimodal AI solutions, integrating video, text, and speech understanding.

- Optimize LLM-powered search, discovery, and content recommendation systems for better user engagement.

- Train and fine-tune deep learning models using TensorFlow, PyTorch, or other ML frameworks.

- Deploy and scale machine learning solutions in a distributed computing environment.

- Work closely with AI researchers, software engineers, and business teams to apply AI technologies effectively.