(General Hire) Machine Learning Engineer Intern (Trust and Safety - CV/NLP/Multimodal LLM) - 2027 Summer

San Jose, California, US Until 10/3/2026 H-1B sponsor history First posted August 4, 2026 Last posted August 4, 2026
Job description

Description

The algorithm team is responsible for developing state-of-the-art computer vision, NLP and multimodality models and algorithms to protect our platform and users from the content and behaviors that violate community guidelines and related regulations. With the continuous efforts from our team, TikTok is able to provide the best user experience and bring joy to everyone in the world.

In our team, you will have the opportunity to participate in the development of the cutting-edge content understanding model to help improve the recognition ability of violated content in TikTok, and will also be responsible for optimizing our distributed model training framework continuously.

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.

Candidates may apply to a maximum of two positions across Our Company and its affiliates globally. Applications will be considered in the order they are submitted.

Applications are reviewed on a rolling basis, so we encourage you to apply early. Please clearly state your availability in your resume, including your start and end dates.

* This opening is part of the general hiring process for the Data-TnS-Algo organization. Applications will be evaluated by multiple teams within the Data-TnS-Algo organization to ensure the best alignment based on skills and interests.

Responsibilities:
- Leverage multimodal large models to explore few-shot and zero-shot strategies for content safety scenarios, and build moderation models with strong generalization capabilities.
- 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.
- Build risk ranking and recall systems to enhance coverage and accuracy in identifying high-risk content.
- Collaborate with product and policy teams to drive real-world deployment and performance optimization of moderation algorithms.

Requirements

Minimum Qualifications:
- Currently pursuing a Master degree with a background in computer science, machine learning, or similar fields;
- Solid foundation in machine learning and deep learning; familiarity with multimodal modeling; hands-on experience with large model projects is a plus.
- Interest in content safety, with an understanding of the challenges in risk identification within moderation workflows.
- Proficient in end-to-end algorithm development, including data processing, modeling, and evaluation; experience with PyTorch or TensorFlow is required.

Preferred Qualifications:
- Strong learning ability, clear communication, and a strong sense of teamwork and responsibility.

For TikTok
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

About this role

Summary

Develop models for content safety, collaborate on deployment, optimize training frameworks.

Job title

Machine Learning Engineer Intern

Experience level

internship

Industry

software

Location requirements

San Jose, CA; remote possible

Salary

Not specified

Visa sponsorship

H-1B sponsor history

Management role

No

Skills & keywords

Required skills

machine learningdeep learningPyTorchTensorFlowdata processing

Preferred skills

large model projectsmultimodal modelingrisk identification

Specializations

computer visionnatural language processingmultimodal modelsdeep learning
Locations

Structured locations inferred from the posting.

San Jose, CA, USA

On-site City

California, USA

Remote State