Algorithm Engineer Intern (TikTok Live Revenue) - 2027 Start (PhD)
TikTok
- Location
- Sydney, New South Wales, Australia
- Employment
- Internship
- Work model
- On-Site
- Level
- Intern
- H-1B history
- 148 approvals (FY2023)
Skills
About this role
TikTok LIVE Revenue: TikTok Live revenue engineering team is responsible for building the cutting-edge revenue ecosystem which includes gifting and other revenue features that are innovative, secure and intuitive for our users. As an important member of the engineering team, you will build solutions that affect millions of users and live streamers.
We are looking for talented individuals to join us for an internship. PhD internships at Our Company provide students with the opportunity to actively contribute to our products and research, as well as to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and professional development events, and collaboration with industry experts. Applications will be reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume (Start date, End date).
As a Intern, you will work on algorithmic solutions that support large-scale LIVE activities for users around the world. You will apply your research background and machine learning expertise to solve real-world business challenges, optimize activity performance, and drive sustainable business growth.
Responsibilities - Build and optimize machine learning systems that provide algorithmic support for global large-scale LIVE activities. - Analyze activity data, develop strategy models, and use data-driven approaches to improve user engagement and business growth. - Design, train, and deploy scalable models and algorithms to support activity recommendation, ranking, targeting, and performance optimization. - Understand business objectives and work closely with product and operations teams to define long-term algorithm strategies and roadmap. - Collaborate with cross-functional teams to continuously improve model performance, experimentation efficiency, and business impact.