Ads ML Systems Intern: Build Large-Scale Data Pipelines

TikTok Inc.; San Jose (CA); On-site; USD 68,000 - 97,000; Full time

Benefits offered by this job

Health insurance

Housing allowance

Paid holidays

Job summary

TikTok is offering a Campus Internship in the Ads Infra team to help build scalable ML data pipelines and platforms for training, serving, and privacy enforcement. Interns will gain hands-on experience with advertising models, data processing, and AI-driven workflows in a global tech environment.

Ideal candidates are PhD students in CS/ML with strong programming skills (C++, Python, Go or Java) and solid knowledge of data structures, algorithms and ML concepts.

Qualifications

• Pursuing a PhD in Computer Science, AI, ML or related field.
• Proficient in at least one language (C++, Python, Go or Java).
• Strong foundation in data structures, algorithms, ML concepts.

Responsibilities

• Develop and optimize advertising recommendation models across training, evaluation, inference and user behavior sequences.
• Build large-scale data pipelines for sample generation, joining, feature processing, storage and data quality.
• Develop ML platforms for distributed training, model distribution, online serving and lifecycle management.
• Explore MLLM applications in advertising, including data production, model training, fine-tuning and inference optimization.
• Apply AI agents to workflow orchestration, resource scheduling, failure diagnosis and automated operations.
• Build ML platforms for GPU allocation, scheduling, usage metering and cost accounting.

Skills

C++

Python

Go

Java

Data structures

Algorithms

Distributed systems

ML platforms

Education

PhD student

Tools

ML frameworks

Job description

TikTok is offering a Campus Internship in the Ads Infra team to help build scalable ML data pipelines and platforms for training, serving, and privacy enforcement. Interns will gain hands-on experience with advertising models, data processing, and AI-driven workflows in a global tech environment.

Ideal candidates are PhD students in CS/ML with strong programming skills (C++, Python, Go or Java) and solid knowledge of data structures, algorithms and ML concepts.