Online Architecture Research Intern (TikTok-Generalized Arch)- 2026 Start (PhD)

ing quality, and development efficiency. Here, you will not only experience how technology directly empowers top-level business, but also deepen your technical expertise in planet-scale architecture research and engineering practices-transforming cutting-edge theory into real-world production solutions. Join us in exploring and driving the evolution of the next-generation planet-scale business architecture!

We are looking for talented individuals to join us for an internship in 2026. PhD Internships at TikTok aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.

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:

• Contribute to research and optimization of TikTok's planet-scale business architecture, supporting billions of users, globally distributed ultra-large-scale data centers, and microservice clusters with millions of services.
• Assist in data modeling and algorithmic optimization to address key challenges in large-scale infrastructure and business architecture, including but not limited to capacity forecasting and planning, service and data placement optimization, traffic scheduling optimization, and risk prediction.
• Under the guidance of mentors, transform research insights into prototypes, tools, or frameworks, and explore their applicability in selected business scenarios.
• Participate in analyzing research data, summarizing experimental results, and contributing to academic publications in top-tier conferences/journals.
• Collaborate with architecture, platform, and business teams to understand real-world production challenges and explore potential optimization opportunities.

Qualifications

Minimum Qualifications:

• Currently pursuing a PhD in Computer Science, Software Engineering, Systems Engineering, Operations Research, Applied Mathematics, or related fields.
• Solid research background in distributed systems, large-scale system optimization, or AI for Systems.
• Able to commit to working for 12 weeks during Summer 2026
• Strong problem-solving skills with the ability to model planet-scale, complex business and infrastructure systems, abstract key variables and constraints, and clearly articulate solutions through rigorous logical reasoning and effective communication.
• Familiarity with optimization algorithms such as integer programming, dynamic programming, graph optimization, heuristic search, or reinforcement learning, with the ability to apply them to large-scale optimization problems.
• Proficiency in at least one mainstream programming language (e.g., Go, Java, C++, Python), with the capability to build research prototypes.
• Ability to quickly learn and deeply understand complex architectural patterns, while leveraging AI tools and automation methods to improve research and development efficiency.

Preferred Qualifications:

• Experience in data modeling and analysis for large-scale systems or business architectures.
• Research publications or submissions to top-tier conferences/journals such as OSDI, SOSP, NSDI, SoCC, SIGCOMM, KDD, NeurIPS.
• Hands-on project experience in distributed systems, resource scheduling, or architecture optimization (including academic projects or previous internships).
• Familiarity with performance and optimization challenges in cross-data-center or cross-region architectures.

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