Senior ML Engineer — Ads Platform for Growth & Impact

TikTok USDS Joint Venture; Los Angeles (CA); On-site; USD 178,000 - 416,000; Full time

Job summary

TikTok USDS Joint Venture LLC in Los Angeles seeks a Machine Learning Engineer/Data Scientist to advance shop advertise experiences by building scalable ML models that improve ad ranking quality and drive advertiser success.

Collaborate with Global counterparts, Product Managers, designers and data engineers to explore new features, evaluate models, and apply causal ML ideas to predict user engagement while maintaining strong data practices and interpretability.

Qualifications

• Proficient in using SQL and Python and experience with data manipulation.
• Experience with big data processing frameworks (e.g., Hadoop, Spark) and distributed computing for efficient data mining on large-scale datasets.
• Solid understanding of machine/deep learning concepts and techniques, including feature engineering, model evaluation, and optimization.
• Strong analytical and problem-solving skills, with a demonstrated ability to handle and derive insights from complex and unstructured datasets.

Responsibilities

• Build highly scalable machine learning systems and state-of-the-art machine learning models to improve ad ranking quality and optimize advertisers' marketing strategies.
• Explore, develop and experiment with new features to improve model accuracy.
• Understand ads platform objectives and take full advantage of modern machine learning to improve ads relevance, quality, and quantity delivered to end-users.
• Collaborate with Global counterpart, Product Managers, Designers, and other disciplines to explore the next generation of shopping experiences on TikTok.

Skills

SQL

Python

Big data frameworks

ML concepts

Analytical skills

Job description

TikTok USDS Joint Venture LLC in Los Angeles seeks a Machine Learning Engineer/Data Scientist to advance shop advertise experiences by building scalable ML models that improve ad ranking quality and drive advertiser success.

Collaborate with Global counterparts, Product Managers, designers and data engineers to explore new features, evaluate models, and apply causal ML ideas to predict user engagement while maintaining strong data practices and interpretability.