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# Machine Learning Engineer, Advertising Auti-Fraud, Pangle-USDS

- **Company:** Tiktok Inc.
- **Location:** San Jose, CA, United States
- **Salary:** $122,574.0 - $259,200.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Cluster Analysis, Apache Hive, Python (Programming Language), Machine Learning, Apache Spark, Deep Learning, Apache Flink, Machine Learning Operations
- **Published:** June 6, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=9950323fcf8ad357

## About the Role

Do you have experience in Predictive modeling analysis?, Minimum Qualifications: - Algorithmic Foundation: Proficiency in classification, clustering, sequence modeling and graph algorithms. - Engineering Skills: Familiar with Python, Go or C++. Knowledge of Hive, Spark or Flink. Skilled at learning and applying industry-leading technologies is a plus. - Strong hands-on practical skills: familiar with problem-analysis, model training and model deployment, and able to apply algorithms to risk control business and verify their effectiveness. - Strong logical thinking and data sensitivity. Demonstrated passion for risk control and adversarial challenges. Excellent communication and teamwork skills. Preferred Qualifications: -Ads relevant experience is a big plus

## Description

About the team Pangle is the ad network of TikTok for Business, helping mobile developers accelerate growth, deliver engaging ad experiences, and maximize revenue by accessing TikTok's global advertising demand. Operating in more than 36 countries and regions, Pangle has built a strong reputation for driving measurable results for app partners worldwide. The Pangle anti-fraud team aims to ensure the security and rights of both ends (advertiser & publisher) of the platform, reduces traffic risks from developers, improves the security of platform traffic, and ensures the delivery experience of advertisers. Responsibilities - Invent, implement, and deploy various forecasting, machine learning, deep learning and graph algorithms, to respond to and mitigate business risks in Pangle. - Improve modeling infrastructures, labels, features and algorithms towards robustness, automation and generalization, reduce modeling and operational load on risk adversaries and new product/risk ramping-ups. -
Collaborate with product, operations, engineering and business backgrounds to enhance current automation risk troubleshooting processes and translate algorithmic capabilities into scalable production systems.


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