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

Seniority: Intern

TikTok is an international short-form video platform seeking a Research Engineer Intern to advance AI-native engineering infrastructure for agentic systems. The role focuses on researching reliable long-horizon agents, building scalable agent platforms, evaluating new methods, and translating research into production systems for software engineering and operations.

Responsibilities

• 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 toolchains, 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

Qualifications

• 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 communication and collaboration skills, with the ability to work across research, infrastructure, platform, and product teams

Preferred Qualifications

• Evidence of research excellence through influential publications, open-source work, deployed systems, or other significant contributions. Relevant venues include NeurIPS, ICML, ICLR, ACL, MLSys, OSDI, SOSP, NSDI, ICSE, and FSE
• Experience building or deploying LLM agents, AI developer tools, or agent platforms in complex or large-scale environments
• Hands-on experience with model post-training, reinforcement learning, execution-feedback loops, tool-using agents, distributed agent runtimes, or scalable evaluation systems
• Experience working with large codebases, distributed systems, CI/CD and testing infrastructure, developer platforms, or production operations
• A track record of translating research into measurable production impact

Skills

• Evidence of research excellence through influential publications, open-source work, deployed systems, or other significant contributions. Relevant venues include NeurIPS, ICML, ICLR, ACL, MLSys, OSDI, SOSP, NSDI, ICSE, and FSE
• Experience building or deploying LLM agents, AI developer tools, or agent platforms in complex or large-scale environments
• Hands-on experience with model post-training, reinforcement learning, execution-feedback loops, tool-using agents, distributed agent runtimes, or scalable evaluation systems
• Experience working with large codebases, distributed systems, CI/CD and testing infrastructure, developer platforms, or production operations
• A track record of translating research into measurable production impact

Tech stack

AI Agents, Large Language Model Reasoning, Reinforcement Learning, Agent Planning and Reasoning, Model Post-Training, Memory and Context Management, Tool Learning, Multi-Agent Systems, Agent Evaluation, Python, C++, Distributed Systems

Benefits

• Interns have day one access to health insurance, life insurance, wellbeing benefits and more.
• Interns also receive 10 paid holidays per year and paid sick time (56 hours if hired in first half of year, 40 if hired in second half of year).
• Interns who are not working 100% remote may also be eligible for housing allowance.