Machine Learning Scientist Intern (TikTok - Content Ecology - LLM application) - 2027 Start (PhD)

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.

We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).

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.