Software Engineer Graduate (Ads Signal & Measurement) - 2027 Start

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. 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. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

• Build signal quality frameworks – anomaly detection, signal recovery, denoising, and correction pipelines that ensure the reliability of advertiser conversion data at scale.
• Contribute to cross-platform identity resolution systems, 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.
• Support 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.
• Help build large-scale experimentation infrastructure and real-time data pipelines powering Conversion Lift, Brand Lift, Split Test, and cross-media measurement products.
• Explore LLM-powered signal intelligence — leverage large language models for semantic understanding of advertiser conversion data, enabling intelligent classification, quality assessment, and automated correction of event signals.
• Collaborate cross-functionally with Product, Data Science, and Infrastructure teams to translate algorithmic ideas into production systems serving advertisers globally.

• Individuals who are completing or have recently completed a Bachelor's/ Master's degree in Computer Science, Statistics, Mathematics, or a related discipline.
• Strong programming skills in at least one of: Python, Go, Java, C/C++.
• Solid foundation in data structures, algorithms, and computer systems fundamentals (e.g., operating systems, networking, databases).
• Foundational knowledge in statistics or machine learning — comfortable with concepts like hypothesis testing, regression, and probabilistic models.
• Strong communication skills and eagerness to learn in a fast-paced environment.

• Backend or data-intensive systems: distributed systems, stream processing, or high-throughput services.
• Signal processing: data quality, anomaly detection, data imputation and denoising.
• Identity resolution: ID mapping, entity resolution, probabilistic matching, graph algorithms.
• Attribution or experimentation: conversion modeling, A/B testing, or causal inference methods.
• Adjacent algorithm domains such as recommendation systems, search ranking, or computational advertising.
• Strong performance in programming or algorithm competitions (e.g., ACM-ICPC, Codeforces, Kaggle).
• Familiarity with large-scale data processing frameworks (e.g., Spark, Flink) or cloud-native infrastructure.
• Genuine curiosity about ads tech and how advertisers think about ROI and measurement.

By submitting an application for this role, you accept and agree to our global applicant privacy policy, which may be accessed here: https://careers.tiktok.com/legal/privacy

The Signal & Measurement team at TikTok Ads is responsible for 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 TikTok. Our work sits at the intersection of distributed systems and causal inference. We operate at massive scale while applying rigorous statistical methodology to answer the hardest question in advertising: "Did this ad actually work?"