ML Engineer Intern — Risk & Integrity (PhD)

InternHunt; San Jose, Northern (CA, KY); Hybrid; USD 68,000 - 97,000; Full time

Benefits offered by this job

Health insurance

Life insurance

Wellbeing benefits

Housing allowance

Paid holidays

Paid sick time

Job summary

TikTok USDS Joint Venture LLC is seeking a Machine Learning Engineer Intern (PhD) to join the Risk & Integrity team in San Jose for a 12-week Summer 2027 program. You will work on large-scale data, collaborate with multi-disciplinary teams, and contribute to end-to-end integrity initiatives.

The role requires a strong ML background with Transformer and GNN experience, plus hands-on coding in Python/C++/Go and knowledge of PyTorch and distributed systems.

Qualifications

• Pursuing a PhD in CS, CE, SE, or related technical field.
• Willing to commit to a 12-week internship in Summer 2027.
• Strong ML foundation; experience with Transformer and GNN for large-scale integrity tasks.
• Proficient in Python, C++, or Go; familiar with distributed computing and PyTorch.

Responsibilities

• Work with large-scale data, logs, metrics, or experiments to understand user search behavior and improve product quality.
• Collaborate with engineering, product, data science, and ML partners to define requirements and deliver milestones.
• Write clear, maintainable code and documentation for assigned workstreams.
• Communicate progress, risks, and learnings with mentors and stakeholders.

Skills

PhD in CS/related

Machine Learning

Transformer

GNN

Python

C++

Go

Spark

Flink

PyTorch

Education

PhD candidate in CS/CE/SE or related

Tools

Spark

Flink

PyTorch

Job description

TikTok USDS Joint Venture LLC is seeking a Machine Learning Engineer Intern (PhD) to join the Risk & Integrity team in San Jose for a 12-week Summer 2027 program. You will work on large-scale data, collaborate with multi-disciplinary teams, and contribute to end-to-end integrity initiatives.

The role requires a strong ML background with Transformer and GNN experience, plus hands-on coding in Python/C++/Go and knowledge of PyTorch and distributed systems.