Machine Learning Engineer Intern (TikTok-Business Risk Integrated Control-Finance Safety) – 2026 Summer (PhD)

Responsibilities

The Business Risk Integrated Control (BRIC) team is missioned to: – Protect TikTok users, including and beyond content consumers, creators, advertisers; – Secure platform health and community experience authenticity; – Build infrastructures, platforms and technologies, as well as to collaborate with many cross-functional teams and stakeholders. The BRIC team works to minimize the damage of inauthentic behaviors on TikTok platforms (e.g. TikTok, CapCut, Resso, Lark), covering multiple classical and novel community and business risk areas such as account integrity, engagement authenticity, anti spam, API abuse, growth fraud, live streaming security and financial safety (ads or e-commerce), etc. In this team you’ll have a unique opportunity to have first-hand exposure to the strategy of the company in key security initiatives, especially in building scalable and robust, intelligent and privacy-safe, secure and product-friendly systems and solutions. Our challenges are not some regular day-to-day technical puzzles — You’ll be part of a team that’s developing novel solutions to first-seen challenges of a non-stop evolvement of a phenomenal product eco-system. The work needs to be fast, transferrable, while still down to the ground to making quick and solid differences. We are looking for talented individuals to join us for an internship in 2026. Internships at TikTok aim to offer students industry exposure and hands-on experience. Turn your ambitions into reality as your inspiration brings infinite opportunities at TikTok. PhD internships at TikTok 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). Responsibilities: 1. Responsible for the research and development of algorithms for AI native applications and optimization of LLM (Large Language Models), driving the exploration and Application of cutting-edge technologies. 2. Enhance the capabilities of natural language understanding based on LLM, such as content understanding/classification, vector retrieval, structuring, and representation learning of short/long texts. 3. Combine the most advanced LLM technology to summarize, understand, and classify user behavior/features. 4. Explore the application of Agent in complex tasks and achieve the application of complex tasks in the risk control field based on LLM.

Qualifications

Minimum Qualifications: – Currently pursuing a PhD in Computer Science, Computer Engineering, Math, Statistics or a related technical discipline – Able to commit to working for 12 weeks during Summer 2026; – Good coding standards, a passion for writing code, and the ability to produce high-quality designs and code. – Excellent problem analysis and problem-solving abilities, capable of identifying the essence of issues from complex data. – Experience in algorithmic research and development of mainstream risk control strategies and LLM systems development experience is a plus. – Strong sense of responsibility, proactive attitude, and good communication and teamwork skills. Preferred Qualifications: – Graduating December 2026 onwards with the intent to return to degree program after the completion of the internship. – Priority will be given to those who master the algorithm principles of LLM (Large Language Models), Fine-tuning, Prompt Engineering, vector databases, and Agent application frameworks such as LangChain/Autogen; familiarity with the latest LLM technologies and experience in training or applying them is a plus. By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy

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