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

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 WWW 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

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 (<10ms) and massive scale (billions of daily predictions)

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover invent simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice ( to know more about how we collect use and transfer the personal data of our candidates.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

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