Machine Learning Engineer Graduate (Ads Signal & Measurement) - 2027 Start

Discover a career that energizes and excites you every day.

@2026 Tik Tok

Technology

Location:

San Jose

Employment Type:

Regular

Job Code:

A91725

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The Signal & Measurement team at Tik Tok Ads owns the full stack of advertising effectiveness — from signal collection and identity resolution to attribution modeling and causal measurement. We build the systems and models that help advertisers worldwide understand and maximize the true business value of their ad spend on Tik Tok.

Our work sits at the intersection of large-scale machine learning and causal inference, applied at massive scale to answer the hardest question in advertising: "Did this ad actually work?" The team's scope covers four pillars: signal, attribution, identity, and measurement. These outputs directly power downstream ads ranking and delivery models, closing the loop from measurement back to optimization. As a new grad on the team, you will be paired with an experienced mentor, own real projects from day one, and grow across both machine learning and large-scale systems.

• Build and improve machine learning models for signal quality — anomaly detection, signal recovery, denoising, and correction — to ensure the reliability of advertiser conversion data at scale.
• Contribute to cross-platform identity resolution: improve 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 — improve conversion prediction and bidding/ranking quality by feeding higher-fidelity signals, resolved identities, and modeled conversions into ads ranking systems, and own the data-to-model feedback loop end to end.
• Help build large-scale experimentation infrastructure and data pipelines powering Conversion Lift, Brand Lift, Split Test, and cross-media measurement products.
• Explore LLM-powered signal intelligence — apply large language models to the semantic understanding of advertiser conversion data, enabling intelligent classification, quality assessment, and automated correction of event signals.
• Collaborate with Product, Data Science, and Infrastructure teams to turn algorithmic ideas into production systems serving advertisers globally.

• 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.
• Solid foundation in machine learning and statistics: comfortable with concepts such as hypothesis testing, regression, probabilistic models, and ideally causal inference.
• Strong programming skills in at least one of Python, Go, Java, or C/C++, with a solid grasp of data structures and algorithms.
• Hands-on ML experience through coursework, research, internships, or personal projects (e.g., training and evaluating models on real-world data).
• 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 (e.g., NeurIPS, ICML, KDD, WWW) or strong performance in algorithm/ML competitions (e.g., ACM-ICPC, Kaggle).
• Experience with large-scale data processing frameworks (e.g., Spark, Flink) or ML…