Applied Science Manager

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.

Responsibilities:

• 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, and reinforcement learning).
• Define the science strategy for ad 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 hired, 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.

Requirements:

• 8+ years of applied research experience.
• 4+ years of scientific or machine learning engineer management experience.
• PhD or master's degree and 8+ years of applied research experience.
• Experience programming in Java, C++, Python, or a related language.
• Experience in several of the following areas: machine learning, statistics, deep learning, natural language processing, or information retrieval.
• 4+ years in managing a team of 5-15 members.

Preferred qualifications:

• Experience building production ML systems at the 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 and 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, and prompt engineering).
• Demonstrated ability to define and drive science roadmaps that influence product and business strategy.