- Unified content understanding. We use multimodal large models for fine-grained semantic parsing of video, building a cross-category, multilingual labeling and event knowledge system that provides reliable foundations for search, recommendation, and safety.
- Platform-level content governance. We develop high-precision recognition and review models that handle billions of pieces of content daily, addressing low-quality and non-compliant material and supporting compliance requirements for goods and content in areas such as local services.
- Business growth and user experience. Through trending discovery, inspiration recommendations, and intelligent customer service, we serve creators and users worldwide, improving distribution efficiency, the precision of creative recommendations, and the speed of customer service response.
We work on understanding hundreds of languages and processing multimodal data at hundred-billion scale, in a global business that keeps growing. You will apply NLP, multimodal, and large model technology at real-world scale, seeing your work reach hundreds of millions of users.
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.
We are seeking exceptional, experienced Research Scientists / Architects in the areas of LLM / Agent Platform. This is a unique role for innovators who are passionate about building the "bones" (scalable infrastructure) of next-generation AI and Agent. You will design and implement novel LLM/Agent frameworks with self-improving capabilities and architect the robust, high-performance infrastructure that brings these LLMs and agents to life at a global scale. This is an opportunity to shape the future of AI at one of the world's most dynamic technology companies.
- Architect and build standardized, configurable, and reusable pipelines for the entire lifecycle of models and agents—from data processing and training to deployment, monitoring, and governance.
- Partner with algorithm teams to understand their needs and provide a world-class infrastructure platform that accelerates their research and development cycles.
- Build robust observability and evaluation frameworks to ensure the reproducibility, reliability, and cost-efficiency of AI workloads at scale.
- Design and implement core platform infrastructure, including model/agent registries, feature stores, and high-throughput retrieval/RAG systems.
- Design, build, and optimize advanced Agentic AI systems, focusing on core components like planning, tool use, and memory.