Research Engineer Intern, Agentic Systems & AI Infrastructure (-Generalized Arch) - 2027

Discover a career that energizes and excites you every day.

@2026 Tik Tok

Location:

Seattle

Employment Type:

Intern

Job Code:

A150756

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Tik Tok is an international short video platform available in over 150 countries and regions, where we aim to inspire creativity and bring joy by helping people discover authentic and interesting moments. Tik Tok has offices around the world, with global headquarters in Los Angeles and Singapore, and additional offices in New York, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo, among others.

The Tik Tok Agentic Arch team is building AI-native engineering infrastructure that transforms how software is designed, built, and operated at Tik Tok scale. We research and deploy reliable long-horizon agents that can reason over large codebases, interact with complex tools and environments, and complete consequential engineering work across distributed production systems.

Our environment provides a unique opportunity to advance agent research through real-world execution feedback. You will develop frontier methods, evaluate them rigorously, and bring them into production through platforms used at large scale—creating measurable improvements in engineering productivity, software quality, and operational effectiveness.

We are looking for talented individuals to join us for an internship. Our internship program offers students hands‑on experience, industry exposure, and opportunities to apply their knowledge to real-world challenges while building a strong foundation for personal and professional growth.

Interns will gain practical experience, explore potential career paths, and participate in social events, learning programs, and development workshops alongside industry professionals.

• Conduct frontier research on reliable long-horizon agents for complex, multi-step software engineering and operations tasks.
• Advance agent capabilities in areas such as hierarchical planning and reasoning, memory and context management, tool use, environment interaction, and multi-agent coordination.
• Co‑design agents and models through post‑training, reinforcement learning, learning from execution feedback, search, and test‑time scaling to improve performance on real‑world tasks.
• Build scalable agent infrastructure for orchestration, evaluation, observability, and reliable execution across large codebases, engineering tool chains, and distributed production environments.
• Apply and validate new methods in representative workflows such as cross‑repository software changes, testing and verification, large‑scale migrations, deployment, and incident diagnosis and remediation.
• Translate research into production systems, define rigorous evaluation methodologies, and measure impact through task success, software quality, engineering efficiency, and system performance.
• Collaborate with researchers, infrastructure teams, developer‑platform teams, and product engineers to deploy solutions at scale and produce publishable research and broader scientific insights where appropriate.

• Currently pursuing an Undergraduate/ Master's in Computer Science or a related field, with relevant work in machine learning, natural language processing, software engineering, distributed systems, programming languages, or a related area.
• Demonstrated research or engineering experience in AI agents or closely related areas such as large language model reasoning, reinforcement learning, program synthesis, or AI for code.
• Strong understanding of one or more relevant areas, including agent planning and reasoning, model post‑training, reinforcement learning, memory and context systems, tool learning, multi‑agent systems, or agent evaluation.
• Strong programming and systems‑building ability in at least one language such as Python, C++, Go, or Java, with the ability to turn research ideas into robust implementations.
• Ability to formulate ambiguous real‑world problems, design rigorous experiments and evaluations, analyze results, and iterate from evidence.
• Strong…