Machine Learning Engineer Intern (Risk & Integrity) - 2027 Summer
Job details
- Employment
- Internship
- Level
- Internship
- Education
- Bachelor's degree
- Posted
- Oct 10, 2026
- Last confirmed open
- Oct 10, 2026
About this role
Public source summary from intern-list.com / Jobright.
TikTok USDS Joint Venture is seeking a Machine Learning Engineer Intern to support its Platform and Community Integrity team. The role focuses on analyzing large-scale data and developing machine learning solutions for risk and integrity challenges, while collaborating with cross-functional partners and communicating project progress and learnings.
Responsibilities
Work with large-scale data, logs, metrics, or experiments to understand user search behavior and improve product or system quality Collaborate with engineering, product, data science, and machine learning partners to define requirements and deliver project milestones Write clear, maintainable code and documentation for assigned workstreams Communicate progress, risks, and learnings with mentors and stakeholders throughout the internship
Qualifications: Currently pursuing a Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related technical field Able to commit to a 12-week internship during Summer 2027 Solid foundation in Machine Learning; proficient in applying Transformer and GNN architectures to solve large-scale integrity challenges with social graph and behavior sequence data.
Experience designing model evaluation systems that balance technical performance with product goals Strong engineering and coding skills.
Proficient in at least one language among Python, C++, or Go; familiar with distributed computing (e.g., Spark, Flink) and deep learning frameworks (e.g., PyTorch) Currently pursuing an Master's degree in Computer Science, Computer Engineering, Software Engineering, or a related technical discipline Agentic AI Experience: Hands-on experience building LLM-based Agents capable of task planning, tool-use, or closed-loop autonomous decision-making LLM Security & Alignment: Experience in LLM Post-training or Fine-tuning (e.g., SFT, RLVR, RLHF) to optimize models for specific risk domains like fraud intent recognition or malicious redirection Multimodal Expertise: Proven track record in video understanding or multimodal representation; ability to integrate visual, audio, and textual features to solve complex behavioral modeling challenges
Benefits: Hands-on experience Industry exposure Opportunities to apply knowledge to real-world challenges Building a strong foundation for personal and professional growth Practical experience Opportunities to explore potential career paths Participation in social events Learning programs Development workshops