Amazon Science
Applied Sciences Manager , Ads Brand Safety and Suitability
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Applied Sciences Manager , Ads Brand Safety and Suitability
Applied Science Manager – Brand Safety and Content Classification
About the Role
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, 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
- 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
- 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
- Emerging content risk vectors
across massive real-time traffic streams, making billions of decisions per day with single-digit millisecond latency constraints.
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This role sits at the intersection of: ✔ Frontier AI research ✔ Large-scale production engineering ✔ Deep science + system-wide impact ✔ 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 misinformation (MFA) trends before they scale
- Balancing precision, recall, latency, explainability, and fairness
- Designing adaptive models resilient to adversarial evolution
- 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 ✅ Tackle open-ended scientific challenges with real-world societal relevance ✅ Reshape what trustworthy, high-quality digital systems look like for the next decade
As AI reshapes the internet, the systems your team builds will define what "trust" means in digital advertising.
Key Job Responsibilities
📢 Vision, Strategy & Roadmap
- Develop the vision, charter, and long-term strategy for Applied Science solutions enhancing contextual ads product
- Drive the strategy and technical roadmap for LLM and ML-based classification systems
- Stay updated on industry landscape trends in contextual advertising to identify algorithm investment priorities
🤝 Team Leadership & Talent Development
- Lead a cross-functional team of Applied Scientists and Software Development Engineers (SDEs)
- Grow a high-performing Applied Science team focused on Brand Safety and AI-driven risk intelligence
- Hire, develop, and mentor senior scientists while accelerating innovation velocity
- Build a culture of innovation, scientific rigor, velocity, and long-term thinking
💻 Technical Execution & Delivery
- Own end-to-end delivery from research & experimentation to high-volume production deployment (millions of classifications per day)
- Establish scalable, automated processes for:
- Large-scale data analysis
- Model development
- Model validation
- Model implementation
- Apply machine learning & statistical techniques to build scalable, high-performance solutions
🧠 Innovation & Frontier Research
- Push boundaries in:
- Multimodal understanding
- Semantic reasoning
- Adaptive learning systems
- Build proactive detection & risk-hunting capabilities for emerging abuse trends
- Continuously research new ML & AI advancements and apply them to Amazon Advertising’s industry-leading solutions
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🤞 Organizational Influence & External Partnerships
- Influence GenAI strategy across Amazon
- Partner closely with:
- Product teams
- Policy & ads quality teams
- Infrastructure teams
- Evangelize new algorithms and drive implementation of large-scale ML models in production
- Work with engineering & product managers to ensure large-scale complex models ship successfully
📊 Business Impact & Thought Leadership
- Drive core business analytics & data science explorations to inform key business decisions & algorithm roadmap
- Publish peer-reviewed papers & whitepapers to showcase technical innovations
Basic Qualifications
- Master’s degree or above in:
- Computer Science
- Mathematics
- Statistics
- Machine Learning
- OR equivalent quantitative field
- OR PhD in related discipline
- Experience managing science teams
- Experience developing, deploying & managing AI products at scale
- Familiarity with Machine Learning & Large Language Model fundamentals, including:
- Model architectures
- Training/inference lifecycle
- Optimization of model execution
- OR experience leading teams or organizations in AI/ML
- Experience working with technical & business stakeholders across global cross-functional teams
- Experience leading large-scale technical/engineering programs with:
- Proven thought leadership
- Business case development
- Customer impact
- Successful program completion
Preferred Qualifications
- Experience in applied research
Amazon’s Commitment to Equality & Inclusion
Amazon is an equal opportunity employer. We believe passionately that diversity fuels innovation. Recruiting decisions are based on your experience and skills, not protected status.
Protecting your privacy is a top priority. Refer to our privacy notice: Amazon Jobs Privacy Policy for details on how we handle candidate data.
Amazon is committed to a policy of affirmative action. We do not discriminate based on:
- Protected veteran status
- Disability
- OR any other legally protected status
Your accommodation needs will be addressed throughout the hiring process. For support, visit: Amazon Accommodations. If your country isn’t listed, contact your Recruiting Partner directly.
Our inclusive culture empowers Amazonians to deliver exceptional results and customer trust. Thank you for applying!
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