Responsibilities

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 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 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. 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. Responsibilities: - 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.

Qualifications

Minimum 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. - 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. Preferred Qualifications: - 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 frameworks (e.g., PyTorch, TensorFlow). - Genuine curiosity about ads tech and how advertisers think about ROI and measurement.

Job Information

[For Pay Transparency]Compensation Description (Annually)

The base salary range for this position in the selected city is $128000 - $256000 annually.

Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.

Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).

The Company reserves the right to modify or change these benefits programs at any time, with or without notice.

For Los Angeles County (unincorporated) Candidates:

Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:

1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;

2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and

3. Exercising sound judgment.