Amazon Ads Brand Safety & Suitability protects advertisers from exposure to unsafe, unsuitable, or policy-violating content across web, mobile app, CTV, and audio advertising inventory. Our mission is to ensure that every ad impression delivered through Amazon's demand-side platform appears adjacent to content that meets advertiser trust expectations while giving brands granular controls to define suitability on their own terms. We operate at the intersection of advertiser trust, publisher quality, and supply integrity.
AI is fundamentally changing the content landscape. Content is now generated at unprecedented scale — faster, cheaper, and increasingly sophisticated. Low-quality, deceptive, AI-generated, and synthetic content evolves in real time, constantly adapting to evade detection. The volume and velocity of new content entering the advertising system has outpaced traditional classification approaches.
We are looking for an Applied Science Manager to lead the next generation of AI-powered Brand Safety and Content Classification systems designed to protect advertisers and elevate supply quality at internet scale. This is not a traditional classification problem. Your team will build systems that make millisecond-level decisions across billions of content signals while continuously adapting to emerging content risks driven by generative AI. You will own the science roadmap for LLM-powered classification and semantic understanding, real-time multimodal content evaluation, adversarial ML and adaptive model resilience, proactive risk intelligence and content risk hunting, AI-generated and synthetic content detection, and large-scale abusive content system identification and disruption.
You will define how modern AI separates high-quality advertising inventory from unsafe, unsuitable, and policy-violating content — across web, mobile app, CTV, and audio surfaces.
What Makes This Role Unique Generative AI has dramatically lowered the cost of producing deceptive, policy-evasive content, and the adversary evolves daily. Your detection systems must reason contextually, adapt rapidly, and generalize beyond previously seen content risk patterns. Static models fail here; you will build living systems that learn and respond in real time. You will do this at internet scale, developing low-latency ML and LLM-powered systems evaluating content safety, brand suitability, misinformation risk, and emerging content risk vectors across massive real-time traffic streams, making billions of decisions per day with single-digit millisecond latency constraints. This role sits at the intersection of frontier AI research and large-scale production engineering, combining deep science, system-wide impact, and business-critical outcomes. The models your team ships directly influence billions of dollars in advertising spend and the trust of the world's largest brands in Amazon DSP.
The Science Problems Are Genuinely Hard You will tackle challenges including detecting sophisticated AI-generated and synthetic content, understanding nuanced contextual brand risk, identifying coordinated MFA space before they scale, balancing precision, recall, latency, explainability, and fairness, designing adaptive models resilient to adversarial evolution, and leveraging LLMs for semantic understanding in real-time, latency-constrained environments.
Why This Matters Few roles offer the opportunity to work at the intersection of frontier AI, internet-scale production systems, adversarial environments, and business-critical impact — while tackling open-ended scientific challenges with real-world societal relevance. As AI reshapes the internet, the systems your team builds will define what trustworthy, high-quality digital systems look like for the next decade.
Key job responsibilities
Vision, Strategy & Roadmap a) Develop the vision, charter, and long-term strategy for Applied Science solutions that enhance critical parts of the contextual ads product. b) Drive the strategy and technical roadmap for LLM and ML-based classification systems. c) Keep updated on the industry landscape in contextual advertising and identify algorithm investments to achieve industry-leading solutions.
Team Leadership & Talent Development a) Lead a cross-functional team of Applied Scientists and SDEs; grow a high-performing Applied Science team focused on Brand Safety and AI-driven risk intelligence. b) Hire, develop, and mentor senior scientists; accelerate the pace of innovation in the group. c)Build a culture of innovation, scientific rigor, velocity, and long-term thinking.
Technical Execution & Delivery a) Drive end-to-end delivery — from research and experimentation through production deployment at billions of classifications per day. b) Establish scalable, efficient, automated processes for large-scale data analyses, model development, model validation, and model implementation. c) Use machine learning and statistical techniques to create new, scalable solutions.
Innovation & Frontier Research a) Push the boundaries of multimodal understanding, semantic reasoning, and adaptive learning systems. b) Build proactive detection and risk-hunting capabilities for emerging abuse trends. c) Continuously learn about new developments in ML and AI; identify how these can be rolled into building industry-leading solutions for Amazon Advertising.
Organizational Influence & Cross-Functional Partnership a) Influence org-wide GenAI strategy; represent the team's technical direction to senior leadership. b) Partner closely with Product, Policy, Ads Quality, and Infrastructure teams to operationalize AI at scale. c) Work proactively with engineering teams and product managers to evangelize new algorithms and drive implementation of large-scale complex ML models in production.
Business Impact & Thought Leadership a) Drive core business analytics and data science explorations to inform key business decisions and algorithm roadmap. b) Showcase innovation via peer-reviewed publications and whitepapers.
Basic Qualifications
- Master's degree or above in computer science, mathematics, statistics, machine learning or equivalent quantitative field, or PhD
- Experience managing science teams
- Experience developing, deploying and managing AI products at scale
- Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience leading and influencing your team or organization
- Experience working with technical and business stakeholders from global cross-functional teams
- Experience in leading large-scale, technical or engineering programs with a proven record of thought leadership, business case development, realizing customer benefits, and successful program completion
Preferred Qualifications
- Experience in applied research
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