Principal Applied Scientist, Supply, Ads

Position: 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 responsibilities

Set the science vision — defining multi-year research directions, establishing the publication roadmap, and driving innovations

Operate across programs — influence modeling frameworks across brand safety, MFA detection, traffic quality, viewability, and 3P integrations; break down silos between science and engineering teams

Act 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 experimentation

Partner with engineering leaders to build efficient, scalable, low-latency production systems that serve models at billions-of-requests-per-day scale

Influence product and business strategy — translate science capabilities into advertiser-facing products (targeting controls, transparency reports, quality guarantees) and quantify business impact

Basic qualifications

Ph.D. in Computer Science, Machine Learning, Statistics, or a highly quantitative field

Experience applying machine learning to real-world problems at scale, with multiple years in a science leadership capacity

Proven 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 systems

Strong publication record in top-tier ML/AI conferences (NeurIPS, ICML, KDD,(Use the "Apply for this Job" box below). ACL, EMNLP, CVPR, or equivalent)

Experience with large-scale distributed ML systems processing terabytes of data Expert-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 outcomes

Preferred qualification

Experience with GenAI/LLM-based classification systems at production scale

Experience in computational advertising, ad tech, content moderation, trust & safety, or fraud/abuse detection

Expertise in adversarial machine learning, anomaly detection, or security-oriented ML applications

Familiarity with industry standards: MRC accreditation, TAG certification, brand safety frameworks, IAB content taxonomy

Experience 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 products

Experience with real-time inference systems operating at low latency (