Machine Learning Engineer Graduate

About the Role

The Signal & Measurement team at TikTok Ads owns the full stack of advertising effectiveness, from signal collection and identity resolution to attribution modeling and causal measurement. We build systems and models that help advertisers understand and maximize the business value of their ad spend on TikTok. Our work combines large-scale machine learning and causal inference applied at massive scale to answer the question: "Did this ad actually work?" The team's scope covers signal, attribution, identity, and measurement, with outputs directly powering downstream ads ranking and delivery models. As a new grad, you will be paired with an experienced mentor, own real projects, and grow in both machine learning and large-scale systems.

Key Responsibilities

• Build and improve machine learning models for signal quality (anomaly detection, signal recovery, denoising, correction) to ensure the reliability of advertiser conversion data.
• Contribute to cross-platform identity resolution, improving the precision and coverage of our Identity Graph through probabilistic matching models and graph algorithms.
• Participate in attribution model design and implementation, including multi-touch attribution (MTA), modeled conversions, and incrementality measurement.
• Drive the downstream application of signal, identity, and attribution data in ranking models, improving conversion prediction and bidding/ranking quality.
• Help build large-scale experimentation infrastructure and data pipelines for products like Conversion Lift, Brand Lift, Split Test, and cross-media measurement.
• Explore LLM-powered signal intelligence, applying large language models to the semantic understanding of advertiser conversion data for classification, quality assessment, and automated correction of event signals.
• Collaborate with Product, Data Science, and Infrastructure teams to turn algorithmic ideas into production systems.

Requirements & Tech Stack

• Solid foundation in machine learning and statistics: hypothesis testing, regression, probabilistic models, and ideally causal inference.
• Strong programming skills in at least one of Python, Go, Java, or C/C++.
• Solid grasp of data structures and algorithms.
• Hands-on ML experience through coursework, research, internships, or personal projects.
• Strong communication skills and eagerness to learn in a fast-paced environment.
• Internship, research, or project experience in one or more of: computational advertising, recommendation/search ranking, causal inference or A/B experimentation, entity resolution/graph learning, anomaly detection, or applied LLMs/NLP.
• Publications at ML/data mining venues or strong performance in algorithm/ML competitions.
• Experience with large-scale data processing frameworks (e.g., Spark, Flink) or ML frameworks (e.g., PyTorch, TensorFlow).
• Genuine curiosity about ads tech and how advertisers think about ROI and measurement.

Qualifications

• Individuals who are completing or have recently completed a Bachelor's or above degree in Computer Science, Statistics, Mathematics, Electrical Engineering, or a related technical discipline.
• Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

---

This job description has been reformatted from the provided raw text. Compensation and benefits details are not included as they were removed per instructions.