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.

Experience
10-14 yrs