About TikTok

TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.

Why Join Us

Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect - and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.

We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.

Diversity & Inclusion

TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.

Job highlights

Positive team atmosphere, Industry experts, Competitive compensation

Responsibilities

Team Introduction:

The Risk Control R&D Team is dedicated to addressing various challenges posed by malicious activities across products. Their work spans multiple domains of risk governance such as content, transactions, traffic, and accounts. By leveraging technologies such as machine learning, multimodal models, and large models, the team strives to understand user behaviors and content, thereby identifying potential risks and issues. By continuously deepening their understanding of business and user behaviors, the team drives innovation in models and algorithms with an aim to build an industry-leading risk control algorithm system.

Project Objectives:

Optimize and enhance large models' ability to understand and reason about structured data (sequential data, graph data) based on risk control data.

Project Necessity:

Data in risk control scenarios is primarily structured, while large models have significantly improved their understanding of text and images. Integrating non-text/image structured data from risk control scenarios with large models to enable better comprehension of structured data remains an industry-wide challenge. This involves three key difficulties:

1. How to effectively align structured information with the NLP semantic space, allowing models to simultaneously understand both data structure and semantic information.

2. How to use appropriate instructions to enable large models to interpret structural information in structured data.

3. How to endow large language models with step-by-step reasoning capabilities for graph learning downstream tasks, thereby inferring more complex relationships and attributes.

Project Content:

Current industry explorations of structured data include:

1. Graph data understanding (e.g., GraphGPT: Enabling large models to read graph data, SIGIR'2024).

2. Graph data RAG (e.g., Microsoft GraphRAG: Unlocking LLM discovery on narrative private data).

3. Sequential data understanding (e.g., StructGPT: A large model reasoning framework for structured data, EMNLP-2023).

However, current efforts mainly focus on understanding single-type structured data, and several challenges remain in risk control scenarios:

1. How to effectively fuse and understand various types of structured data, especially the integration of graph and sequential data.

2. Addressing the challenges mentioned in the ""Project Necessity"" section, particularly the step-by-step reasoning capabilities for downstream tasks, which are currently underexplored-especially reasoning over sequential data.

Research Directions:

1. Large model structured data understanding

2. Large model structured data RAG

3. Large model thought chains

Qualifications

1. Got doctor degree, currently pursuing a doctoral degree in computer science, cybersecurity, artificial intelligence, or related fields.

2. Excellent coding skills and a solid foundation in data structures and algorithms; proficiency in Python is required, and familiarity with PyTorch or TensorFlow (TF) is preferred.

3. Outstanding ability to define, analyze, and solve problems; candidates with publications in CCF-A category journals or top conferences such as AAAI, NeurIPS, SIGKDD, SIGIR, etc., are preferred.

4. Strong resilience under pressure, excellent communication and teamwork skills; passionate about technology, willing to embrace challenges with the team, and a drive for innovation.