Principal Applied Scientist, Trusted Supply, Amazon Ads

Amazon Advertising is a fast-growing multi-billion dollar business that spans desktop, mobile, and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third-party publishers; and extends across US, EU, and an expanding number of international geographies. The Trusted Supply organization has the charter to safeguard advertiser trust and ensure high-quality ad impressions across all Amazon Advertising surfaces.

We develop advanced algorithms and infrastructure systems to protect advertisers from unsafe content adjacency, low-quality inventory, fraud and privacy threats. Our scope spans a wide variety of problems in computational advertising including brand safety classification, content suitability scoring, risk hunting and proactive threat detection, viewability prediction, Made-for-Advertising (MFA) detection, malvertising identification, and privacy-preserving measurement and integration.

We are looking for an exceptional Principal Applied Scientist to define and drive the science vision across Brand Safety, Suitability, and Risk Hunting as primary areas of focus, while contributing to broader Supply Quality challenges around viewability, privacy-preserving solutions, and data leakage prevention. This is a high-visibility leadership role where your models and systems will process billions of ad impressions daily, directly impacting advertiser confidence, customer experience, and a multi-billion dollar business.

Key job responsibilitiesSet the science vision — defining multi-year research directions, establishing the publication roadmap, and driving innovationsOperate across programs — influence modeling frameworks across brand safety, MFA detection, traffic quality, viewability, and 3P integrations; break down silos between science and engineering teamsAct as a thought leader — anticipate industry shifts (privacy regulations, adversarial evolution, GenAI-powered threats), propose counter-strategies before they become critical, and represent Amazon in industry forums (TAG, MRC, IAB)Hire, mentor, and grow a high-performing team of applied scientists and research engineers; establish a culture of scientific rigor, peer-reviewed publications, and rapid experimentationPartner with engineering leaders to build efficient, scalable, low-latency production systems that serve models at billions-of-requests-per-day scaleInfluence product and business strategy — translate science capabilities into advertiser-facing products (targeting controls, transparency reports, quality guarantees) and quantify business impactBasic qualificationsPh.

D. in Computer Science, Machine Learning, Statistics, or a highly quantitative fieldExperience applying machine learning to real-world problems at scale, with multiple years in a science leadership capacityProven track record of leading, mentoring, and growing teams of scientists (5+ scientists)Deep expertise in NLP, Computer Vision, or multi-modal learning with demonstrated impact in production systemsStrong publication record in top-tier ML/AI conferences (NeurIPS, ICML, KDD, WWW, ACL, EMNLP, CVPR, or equivalent)Experience with large-scale distributed ML systems processing terabytes of dataExpert-level proficiency in Python and at least one systems language (Java, C++, Scala)Demonstrated ability to translate ambiguous business problems into well-defined science initiatives with measurable outcomesPreferred qualification Experience with GenAI/LLM-based classification systems at production scaleExperience in computational advertising, ad tech, content moderation, trust & safety, or fraud/abuse detectionExpertise in adversarial machine learning, anomaly detection, or security-oriented ML applicationsFamiliarity with industry standards: MRC accreditation, TAG certification, brand safety frameworks, IAB content taxonomyExperience with privacy-preserving ML techniques (federated learning, differential privacy, on-device inference)Track record of defining org-level research practices and shipping 0-to-1 science productsExperience with real-time inference systems operating at low latency (<10ms) and massive scale (billions of daily predictions)Amazon is an equal opportunities employer.

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