Applied Science Manager, Advertising Trust

Amazon; India; On-site; INR 6,000,000 - 9,000,000; Full time

Job summary

Amazon Ads invites applications for an Applied Science Manager in Advertising Trust. You will lead a team of applied scientists building next‑generation content moderation intelligence, owning the science roadmap for a high‑impact automation program across global marketplaces.

You will define the strategy for multimodal understanding, retrieval‑first classification, and LLM‑based reasoning, driving end‑to‑end ML delivery and measurable improvements in automation and defect metrics for senior

Qualifications

• 8+ years of applied research experience.
• 4+ years of scientists or machine learning engineers management experience.
• 4+ yrs in managing team of 5-15 members.

Responsibilities

• Lead a team of applied scientists across multimodal ML, retrieval systems, and generative AI.
• Define the science strategy for ads trust and moderation.
• Own end-to-end delivery of ML solutions from problem formulation to online testing and deployment.
• Hire, mentor, and develop team members to raise the science bar.
• Partner with engineering, product, and operations to translate science investments into measurable automation improvements.
• Communicate science strategy and results to senior leadership through narratives and roadmaps.

Skills

Applied research leadership

People management

Team leadership (5-15)

Job description

Applied Science Manager, Advertising Trust

Amazon Ads delivers advertising experiences across Amazon's owned-and-operated properties and third-party networks, reaching hundreds of millions of customers worldwide. Within Amazon Ads, Advertising Trust is the science-first organization responsible for ensuring every ad shown to customers meets Amazon's content policies — at massive scale, across all ad formats and global marketplaces.

The Ads Trust Science team builds the ML systems that automate content moderation decisions: multimodal classification, retrieval-based labeling, LLM reasoning, and agentic self-improvement architectures. This requires inventing new approaches at the intersection of computer vision, NLP, information retrieval, and generative AI.

We are seeking an Applied Science Manager to lead a team of applied scientists building next-generation content moderation intelligence. You will own the science roadmap for one of the highest-impact automation programs in Amazon Advertising, defining how multimodal content understanding, retrieval-first classification, and LLM-based reasoning combine into a production system that serves global advertising at scale.

• Lead a team of applied scientists working across multimodal ML (vision-language models, video understanding), large-scale retrieval systems (embedding-based similarity and deduplication), and generative AI (LLM-based policy reasoning, knowledge distillation, agentic architectures, reinforcement learning).
• Define the science strategy for ads trust.
• Own end-to-end delivery of ML solutions: problem formulation, offline experimentation, online A/B testing, and production deployment. Your models directly move automation and defect metrics reported to senior leadership.
• Build and grow scientists — hire, mentor, and develop team members. Raise the science bar through structured review processes and a publication culture within Amazon.
• Partner with engineering, product, and operations teams to translate science investments into measurable automation improvements. Influence roadmaps across dependent teams.
• Communicate science strategy and results to senior leadership through narratives, technical deep-dives, and roadmap documents.

Basic Qualifications

• 8+ years of applied research experience
• 4+ years of scientists or machine learning engineers management experience
• 4+ yrs in managing team of 5-15 members

Preferred Qualifications

• Experience building production ML systems at Internet scale, especially involving multimodal deep learning, generative AI, or large-scale retrieval
• Track record of delivering automation or classification systems with measurable business impact
• Experience with content moderation, trust & safety, or policy enforcement systems
• Publications in top-tier ML/AI venues (NeurIPS, ICML, CVPR, KDD, ACL, AAAI)
• Experience with LLMs (fine-tuning, distillation, RLHF, prompt engineering)
• Demonstrated ability to define and drive science roadmaps that influence product and business strategy

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