Machine Learning Engineer Graduate (TikTok Short Video Content Understanding/Multimodal Recommendation) - 2026 Start (BS/MS)

Our team's mission is to empower content understanding for TikTok Short Video business. We focus on cutting-edge research in content understanding and the development of advanced LLM/MLLM algorithms and applications, including generative recommendation, weakly-supervised learning, few-shot classification, video tagging, multi-task learning, multilingual learning, multimodal pretraining, and more. We aim to succeed both in driving measurable business impact (e.g., recommendation metrics) and delivering state-of-the-art research outputs.



We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at TikTok.



Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume.



Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to TikTok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.



Responsibilities

1. Lead multimodal algorithm development for TikTok’s short-video business, explore applications of multimodal technologies in recommendation systems and other scenarios to improve key business metrics.

2. Conduct cutting-edge research in multimodal and MLLM technologies, design advanced algorithms to solve business requirements while achieving technical breakthroughs.

3. Drive engineering deployment and implementation, ensuring model stability, scalability, and efficiency in production environments.

4. Focus on key areas including (but not limited to):

- General AI platform design and development, including few-shot/zero-shot on MLLM, AI-labeling, auto prompting, active-learning, continue pretraining and RL.

- Integration of content understanding with recommendation systems (e.g., UGC ecosystems, cold start, interest exploration, comment understanding).

- Leveraging multimodal techniques to develop next-generation recommendation systems, such as generative models and end-to-end approaches.