Multimodal ML Engineer Intern: Content Safety & Vision

TikTok; San Jose (CA); On-site; USD 52,000 - 72,000; Part time

Benefits offered by this job

Health insurance

Life insurance

Wellbeing benefits

10 paid holidays per year

Paid sick time

Housing allowance

Job summary

TikTok is offering a campus internship in our California offices focused on building state-of-the-art computer vision, NLP and multimodal models for content safety. You will work on cutting-edge model development, training optimization, and evaluation in a collaborative team environment.

Ideal candidates are pursuing a Master in CS/ML with hands-on experience in PyTorch or TensorFlow, and a strong interest in risk identification within moderation workflows.

Qualifications

• Currently pursuing a Master degree in computer science, machine learning, or a related field.
• Solid foundation in machine learning and deep learning; familiarity with multimodal modeling; hands-on with large model projects is a plus.
• Proficient in end-to-end algorithm development, including data processing, modeling, and evaluation; experience with PyTorch or TensorFlow is required.

Responsibilities

• Develop state-of-the-art computer vision, NLP and multimodal models and algorithms for content safety.
• Optimize distributed model training frameworks and contribute to scalable training pipelines.
• Collaborate with product and policy teams to deploy moderation algorithms in real-world settings.

Skills

ML basics

Deep learning

Multimodal modeling

End-to-end algorithm development

Education

Master degree in CS/ML or related

Tools

PyTorch

TensorFlow

Job description

TikTok is offering a campus internship in our California offices focused on building state-of-the-art computer vision, NLP and multimodal models for content safety. You will work on cutting-edge model development, training optimization, and evaluation in a collaborative team environment.

Ideal candidates are pursuing a Master in CS/ML with hands-on experience in PyTorch or TensorFlow, and a strong interest in risk identification within moderation workflows.