短视频内容理解高级机器学习工程师

Our team's mission is to empower content understanding for TikTok Short Video business.

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.

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.

- 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.

1. Proven experience in multimodal content understanding, with expertise in large language models (LLMs) and familiarity with cutting-edge progress in the field.

2. Strong technical foundation in at least one major deep learning framework (e.g., PyTorch, TensorFlow).

3. Proactive mindset, strong sense of ownership, excellent communication skills, and ability to collaborate across teams.

1. Hands-on experience deploying content understanding solutions in search, advertising, recommendation, or related domains.