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Research Scientist Intern- (Trust and Safety - CV/NLP/Multimodal LLM) - 2026 Start(PhD)

TikTok

Research Scientist Intern- (Trust and Safety - CV/NLP/Multimodal LLM) - 2026 Start(PhD)

TikTok

San Jose, CA

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

Flexible work arrangements

Generous paid time off and holidays

Comprehensive health, dental, and vision insurance

Parental leave

Flexible Hours

Healthcare

Parental Leave

Required Skills

Node.js

Python

PostgreSQL

Responsibilities

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, Tik Tok 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 Tik Tok, 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 in 2026. PhD Internships at Tik Tok aim to provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies.

PhD internships at Tik Tok provide students with the opportunity to actively contribute to our products and research, and to the organization's future plans and emerging technologies. Our dynamic internship experience blends hands-on learning, enriching community-building and development events, and collaboration with industry experts.

Applications will be reviewed on a rolling basis - we encourage you to apply early. Please state your availability clearly in your resume (Start date, End date).

Key Responsibilities

  • Develop computer vision model or multimodality model to recognize violation content in Tik Tok
  • Explore cutting-edge multimodal or computer vision large models (CLIP, COCA, ALBEF, BLIP, Flamingo, ViT-G, ViT-22B, EVA-enormous, etc)
  • Explore the application of LLM in our business scenarios, like pre-training, zero-shot/ few-shot learning, hard case mining, etc
  • Continuously optimize the training framework to better adapt to the training of large models

Qualifications

Minimum Qualifications

  • PhD candidate in computer science or a related technical discipline
  • Related Research Experience at least one of the following areas: computer vision, multimodality, LLM
  • Be proficient with at least one deep learning framework (e.g. Py Torch, Tensor Flow)
  • Have excellent analytical and problem-solving skills, logical thinking skills, communication and collaboration skills

Preferred Qualifications

  • Published papers in the top AI conferences or journals is a plus, including CVPR, ICCV, ECCV, NIPS, ICML, ICLR, TPAMI, IJCV, etc

Privacy

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

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About TikTok

TikTok

TikTok

Late Stage

A short-form video entertainment app and social network platform

10,001+

Employees

Los Angeles

Headquarters

$220B

Valuation

Reviews

3.1

3 reviews

Work Life Balance

1.5

Compensation

2.0

Culture

1.2

Career

1.8

Management

1.0

5%

Recommend to a Friend

Pros

Limited positive feedback available

Company size allows for potential opportunities

Technology platform experience

Cons

Mass layoffs and poor handling of terminations

Unprofessional management and HR behavior

Exposure to traumatic content without adequate support

Salary Ranges

52 data points

Mid/L4

Senior/L5

Mid/L4 · Applied AI Product Data Scientist

1 reports

$273,000

total / year

Base

$210,000

Stock

-

Bonus

-

$273,000

$273,000

Interview Experience

4 interviews

Difficulty

3.5

/ 5

Duration

21-35 weeks

Experience

Positive 0%

Neutral 25%

Negative 75%

Interview Process

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Interviews

6

Team Matching

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Data Structures