ML Engineer - E‑Commerce Risk & Fraud

TikTok USDS Joint Venture; Seattle (WA); On-site; USD 130,000 - 246,000; Full time

Job summary

TikTok USDS Joint Venture LLC is seeking an experienced machine learning specialist to join the E-Commerce Risk Control team, focusing on detecting fraud, abuse, and malicious activity on TikTok E-Commerce platforms. You'll work on building scalable, privacy-safe systems in a fast-evolving product ecosystem.

On-site in Seattle, you will collaborate with security and data science teams to deploy models, analyze risk signals, and iterate quickly to reduce losses while preserving user experience.

Qualifications

• Master’s degree or higher in a relevant STEM field.
• 1+ years hands-on experience building ML models for large-scale internet projects.
• Proficient in C++, Java, Python with ML/DL frameworks.
• Experience in ecommerce fraud or risk control with detection models.
• Strong data-driven, autonomous, results-oriented mindset.

Responsibilities

• Invent, implement, and deploy ML algorithms to mitigate ecommerce risks.
• Use representation learning, graph models, deep learning and transfer learning to improve detection.
• Monitor risk metrics and proactively identify attacks, drive risk governance.
• Mine and analyze large ecommerce data to improve model accuracy and robustness.
• Advance privacy, interpretability, and scalable ML for risk control.

Skills

Machine learning

C++

Java

Python

Fraud detection

Education

Master’s degree in Computer Science, Mathematics, Machine Learning, or related STEM fields

PhD in Computer Science, Machine Learning, Statistics, Operations Research, or related fields

Job description

TikTok USDS Joint Venture LLC is seeking an experienced machine learning specialist to join the E-Commerce Risk Control team, focusing on detecting fraud, abuse, and malicious activity on TikTok E-Commerce platforms. You'll work on building scalable, privacy-safe systems in a fast-evolving product ecosystem.

On-site in Seattle, you will collaborate with security and data science teams to deploy models, analyze risk signals, and iterate quickly to reduce losses while preserving user experience.