Research Scientist Intern (Ads Integrity) - 2026 Start (PhD)

TikTok Ads Business Integrity team has a strong user focus and a dedication to technical excellence. We aim to meet our users’ needs with reliable and high-performing platforms and services. We are excited to grow our advertisers' and users' business understanding, build highly scalable machine learning models, and partner across disciplines with global teams, in pursuit of excellence. Given the fast growth of TikTok in the world, we are working on building a next-generation content understanding system for TikTok monetization.

We are looking for talented individuals to join us for an internship in 2026. Internships at Tikok aim to offer students industry exposure and hands-on experience. Watch your ambitions become reality as your inspiration brings infinite opportunities at Tiktok

PhD internships at Tikok provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

Summer Start Dates:
May 11th, 2026
May 18th, 2026
May 26th, 2026
June 8th, 2026
June 22nd, 2026

Responsibilities:
1. Lead research and development of advanced generative AI technologies, including LLMs, multimodal models (text/image/video), and deepfake detection/synthesis, focusing on optimizing performance across pre-training, SFT, RLHF, and AI safety.
2. Design and deploy cutting-edge AIGC solutions for content understanding and monetization in diverse applications such as ads, e-commerce, short video, and live streaming, contributing to the creation of next-generation AI-driven ecosystems.
3. Drive advancements in LLM-based agents using reinforcement learning to enable autonomous reasoning, planning, and interactive capabilities, addressing real-world challenges in dynamic environments.
4. Innovate techniques to improve the efficiency of large-scale model training and inference, including distillation, quantization, and speculative decoding, for scalable and practical deployment in production.
5. Collaborate with interdisciplinary teams to transition research breakthroughs into production-grade AI services, ensuring robust, low-latency, and cost-effective solutions.
6. Stay at the forefront of generative AI research by contributing to patents, publications, and open-source projects, while actively monitoring and contributing to the latest industry trends and innovations.