Algorithm Engineer Intern – Content Safety AI

Job Description

Machine Learning Engineer Intern (Trust and Safety – CV/NLP/Multimodal LLM) – 2026 Summer(BS/MS) | TikTok

The Tone:
This is a 12-week internship at TikTok. While a specific work location for this role is not explicitly stated within the core job description, pay transparency information references Los Angeles County, which is also home to one of TikTok’s global headquarters. TikTok operates as the leading destination for short-form mobile video, with a core mission to inspire creativity and bring joy to its global user base. This role within the algorithm team is vital for developing state-of-the-art computer vision, NLP, and multimodality models and algorithms, directly contributing to protecting the platform and its users from content and behaviors that violate community guidelines and related regulations. The work in this position directly supports TikTok’s ability to provide a safe and positive user experience for everyone.

The TL;DR
• Role: Internship
• Type: Temporary
• Location: In-person, Los Angeles, CA
• Pay: $45 hourly
• Team: Algorithm team, responsible for developing computer vision, NLP, and multimodality models and algorithms
• Mission: Develop and optimize advanced content understanding models to identify and mitigate violating content and behaviors, ensuring user safety and platform integrity.
• Tech Stack: PyTorch, TensorFlow

What You’ll Actually Do
• Model Exploration: Leverage multimodal large models to explore few-shot and zero-shot strategies for content safety scenarios, building moderation models with strong generalization capabilities.
• Data Mining & Design: Participate in reinforcement learning–based data mining, and help design Chain-of-Thought (CoT) annotation frameworks to improve the model’s understanding of complex risks.
• System Development: Build risk ranking and recall systems to enhance coverage and accuracy in identifying high-risk content.
• Collaboration & Deployment: Collaborate with product and policy teams to drive real-world deployment and performance optimization of moderation algorithms.
• Framework Optimization: Continuously optimize the distributed model training framework used by the team.

The Must-Haves
• Background: Currently pursuing a Master’s degree in computer science, machine learning, or similar fields. This position is open to students aiming to apply their academic knowledge in a real-world setting, focusing on content safety and advanced algorithmic development.
• Experience: Possess a solid foundation in both machine learning and deep learning principles, with specific familiarity in multimodal modeling. Candidates should demonstrate hands-on experience in end-to-end algorithm development, encompassing data processing, model construction, and performance evaluation.
• Skills: Required proficiency in at least one major deep learning framework, such as PyTorch or TensorFlow. Candidates must show a keen interest in content safety and possess an understanding of the unique challenges involved in identifying risks within moderation workflows.
• Bonus: Hands-on experience with large model projects is a significant advantage. The ideal candidate will also exhibit a strong learning ability, clear communication skills, and a commitment to teamwork and responsibility.