Applied Scientist, Traffic Quality

About the Role

com), third-party publisher networks, and emerging channels like generative AI-powered shopping experiences. As one of the fastest-growing segments of Amazon, we operate at unprecedented scale across desktop, mobile, connected TV, and emerging surfaces. Within Amazon Ads, Traffic Quality is a critical pillar of advertiser trust and marketplace integrity.

Our mission is to build advanced capabilities that work at petabyte scale to detect sophisticated invalid traffic (IVT) which includes sophisticated non-human traffic, bot networks, and fraudulent engagement patterns across programmatic advertising.

We are on a journey to establish Amazon Ads as an industry leader in traffic quality standards and transparency.

Our research agenda focuses on staying ahead of adversarial actors through continuous innovation in detection methodologies, leveraging state-of-the-art techniques in deep learning and generative modeling, user behavior and multi-modal representation learning, anomaly detection, time-series analysis, and sparse labeling methods.

We process billions of ad events daily, developing novel algorithms that balance precision and recall while operating under strict latency constraints. Our work directly protects hundreds of millions of dollars in advertiser spend annually while maintaining a seamless user experience.

Key job responsibilities As an Applied Scientist II in Traffic Quality, you will solve inherently hard problems in advertising fraud detection using deep learning, self-supervised techniques, representation learning, and advanced clustering.

You'll work on systems that process billions of ad impressions and clicks per day, using Amazon's cloud services including EC2, S3, EMR, Sagemaker, and RedShift. - Define and frame new research problems in fraud detection where neither problem nor solution is well-defined.

- Invent and adapt new machine learning approaches, models, and algorithms to detect sophisticated invalid traffic. - Design and deploy production-quality ML components that directly impact advertiser trust and the business top-line. - Apply domain knowledge to perform broad data analysis as a precursor to modeling and build business insights.

- Work with unstructured and massive datasets to deliver results. - Produce research reports meeting top-tier external publication standards. - Contribute to the scientific community through publications at peer-reviewed venues and reviewing research submissions. - Mentor and develop junior scientists on the team.

About the team Here are a few papers published by the team: 1/ [Scaling Generative Pre-training for User Ad Activity Sequences.

]( 2/ [SLIDR: Real-time Robot Detection On Online Ads, IAAI 2023, Deployed Highly Innovative Applications of AI Track (AAAI 2023)]( 3/ [Self-supervised Representation Learning Across Sequential and Tabular Features Using Transformers, NeurIPS 2022, First Table Representation Learning Workshop]( - 3+ years of building models for business application experience - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

Job Details

Experience: in patents or publications at top-tier peer-reviewed conferences or journals

Experience: in solving business problems through machine learning, data mining and statistical algorithms

Experience: in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing - 2+ years of hands-on predictive modeling and large data analysis experience - 2+ years of designing experiments and statistical analysis of results experience Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.