# Senior Applied Scientist, Brand Protection Machine Learning at Amazon

Amazon is hiring a Senior Applied Scientist, Brand Protection Machine Learning in Seattle, United States. It pays $167k-$226k a year and Level rates it Builds AI ●●●●; you can [apply on Amazon](https://jobsbylevel.com/go/1a636e1d-f37a-40d8-93a9-3fe48310e119?utm_medium=ai-agent).

AI Level 4, AI centrality 97 out of 100. US, WA, Seattle.

## Details

- Company: [Amazon](https://jobsbylevel.com/companies/amazon)
- AI level: AI Level 4 (score 97 out of 100)
- Location: US, WA, Seattle
- Salary: $167k-$226k
- Posted: November 4, 2026
- Apply: https://jobsbylevel.com/go/1a636e1d-f37a-40d8-93a9-3fe48310e119?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-applied-scientist-brand-protection-machine-learning-at-amazon-614864

## Prepare for this job

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## Description

Do you want to tackle scientifically complex problems that protect millions of brands, sellers, and customers worldwide? Do you want to see your AI solutions act on billions of product listings and have a real impact on how Amazon safeguards its marketplace? The Brand Protection team at Amazon builds high-performance AI systems using machine learning, computer vision, and Large Language Models (LLM) to identify and prevent abuse and counterfeit activity on behalf of brand owners globally. We are at an exciting inflection point, scaling our proven multi-modality solutions to an even broader set of challenges. As a Senior Applied Scientist on this team, you will design and execute the science strategy for complex, ambiguous problems. You will develop multi-modal and LLM-based approaches that extract key information from product detail pages, assist human decision-making, and ultimately enable autonomous decisions at scale. You will partner closely with junior scientists, engineers, and product teams to take solutions from research through production, owning the end-to-end lifecycle. Your work will directly shape how Amazon protects the integrity of its marketplace. Key job responsibilities Design and execute the science strategy for complex brand-protection problems, selecting the right combination of computer vision, multi-modality, and LLM techniques to deliver production-ready solutions at scale. Collaborate with product managers, engineering partners, and fellow scientists to translate business challenges into well-defined machine learning experiments, iterating from prototype to launch. Analyze large volumes of structured and unstructured data to surface insights, inform model design, and continuously improve detection accuracy. Mentor junior scientists through code reviews, experiment design guidance, and technical discussions, raising the overall scientific rigor of the team. Publish research findings in top-tier journals and conferences, contributing to the broader scientific community while advancing Amazon's brand-protection capabilities. A day in the life You will split your time between hands-on research and collaboration. A typical stretch includes designing and running experiments on multi-modal models, reviewing results with product and engineering partners, and brainstorming next steps in working sessions. You operate proactively, driving your own roadmap rather than reacting to incoming requests, while regularly syncing with stakeholders to keep your science strategy aligned with business priorities. There are no direct reports; your influence comes through technical leadership and mentorship. About the team The Brand Protection applied science team is a centralized group of approximately 20 scientists with PhDs from a wide range of quantitative backgrounds. We sit within Selling Partner Services and work closely with product, engineering, and fellow science teams to build AI systems that safeguard brand owners and customers across Amazon's marketplace. You will have the opportunity to work with generative AI and the latest multi-modality models, and your innovations can lead to publications in top journals — making this a place where applied research and customer impact go hand in hand. Basic qualifications - PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 4+ years of building machine learning models or developing algorithms for business application experience - 3+ years of building machine learning models for business application experience - Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects) - Experience with neural deep learning methods and machine learning - Experience programming in Java, C++, Python or related language - PhD, or Master's degree and 6+ years of

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