Applied Science Manager, Seller Partner Trust and Store Integrity Science - Global Risk Intelligence and Prevention

Amazon is hiring an Applied Science Manager, Seller Partner Trust and Store Integrity Science - Global Risk Intelligence and Prevention in Seattle, United States. It pays $184k-$249k a year and Level rates it ; you can apply on Level.

AI in this role

computer-visionnlp

We are looking for an Applied Science Manager III to lead a team of applied scientists building next-generation AI-powered seller risk detection systems. This role sits at the intersection of deep technical science, strategic planning, and cross-organizational partnership. GRIP Science serves a complex stakeholder landscape: enforcement teams, policy owners, marketplace operations, peer science orgs, and senior leadership, each with distinct timelines and priorities. The right leader for this role will navigate that landscape with clarity, balancing urgent operational needs against the long-term scientific investments required to stay ahead of adversarial actors at scale.

You will independently own the vision, roadmap, and execution for a critical detection surface, defining what to build, what to defer, and how to measure success. You will set and maintain a high bar for scientific rigor while cultivating a team culture that rewards creative problem-solving and principled risk-taking.

Key job responsibilities
Strategy & Stakeholder Partnership

• Define the scientific vision and multi-year roadmap for your team's detection domain, ensuring alignment across a broad set of stakeholders with competing priorities and timelines
• Develop strong working relationships with enforcement, policy, operations, and engineering partners; serve as the trusted scientific voice in cross-functional discussions
• Exercise judgment in sequencing work. Distinguish between high-urgency requests that warrant immediate response and those better addressed through systematic, durable solutions
• Influence org-level planning (OP1/OP2) through compelling written narratives and data-driven proposals that articulate both near-term deliverables and long-term scientific bets
• Proactively communicate trade-offs to leadership and stakeholders, building trust through transparency about what your team will and will not prioritize in a given cycle

Science & Technical Leadership

• Lead a team building state-of-the-art detection models that leverage modern AI techniques, including deep learning, foundation models, and generative AI, to identify novel abuse patterns at scale
• Set and uphold a high standard for scientific methodology: model validation, experimentation rigor, reproducibility, and interpretability of results
• Evaluate end-to-end scientific proposals for soundness, scalability, and production readiness; know when to push for deeper investigation and when to ship
• Foster a culture of scientific curiosity and invention. Encourage the team to challenge assumptions, explore out of the box approaches, and invest in research that creates step-function improvements rather than incremental gains
• Identify where emerging AI capabilities (e.g., large language models, multimodal reasoning, self-supervised learning) can be applied to unlock new detection signals or fundamentally reshape existing pipelines
• Make informed trade-offs between model sophistication, delivery timelines, and operational sustainability

Execution & Operational Excellence

• Scope scientific projects, set priorities, and drive schedules; readjust decisively when incoming signals warrant a change in direction
• Structure work to reduce exposure to classic failure modes: ambiguous requirements, untested assumptions, under-instrumented models
• Establish mechanisms and metrics to continuously measure model effectiveness, enforcement precision, and downstream business outcomes
• Drive production readiness: monitoring, alerting, on-call practices, and rapid response for AI/ML systems processing millions of daily decisions

Team Building & Development

• Build and lead a high-performing team of applied scientists; attract and develop talent that combines technical depth with ownership mindset
• Invest in the growth of individual scientists through deliberate project assignment, regular coaching, and actionable feedback tied to career progression
• Create an environment where scientists have the space to do deep work, the support to take calculated risks, and the recognition when they deliver
• Manage performance across the full spectrum. Accelerate top performers toward promotion readiness while addressing gaps constructively and with clear expectations

About the team
GRIP Science (Global Risk Intelligence & Prevention) is the core detection engine within TSI Science. We build and operate state-of-the-art ML and AI models that identify and block bad sellers across Amazon's 24 global marketplaces. Our systems cover the full seller journey, from registration through every action on the marketplace, and score holistic risk across all active sellers. We push the frontier of adversarial detection by combining large-scale deep learning, graph-based reasoning, generative AI, and real-time inference to outpace bad actors who continuously evolve their tactics. This is one of the most technically demanding and high-impact applied science problem spaces at Amazon. Your models directly shape marketplace integrity for hundreds of millions of customers worldwide.

Basic qualifications

- 3+ years of scientists or machine learning engineers management experience
- Knowledge of ML, NLP, Information Retrieval and Analytics
- Ph.D. in computer science, machine learning, engineering, or related fields, or PhD and 5+ years of a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science experience
- 1+ years of scientists or machine learning engineers management experience
- Experience hiring and leading experienced scientists as well as having a successful record of developing junior members from academia or industry to a successful career track
- Experience working and communicating with multiple stakeholders, C-level executives and cross-functional teams
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Experience in written and verbal communication skills to communicate with technical and non-technical audiences, including senior leadership

Preferred qualifications

- Experience building machine learning models or developing algorithms for business application
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 183,800.00 - 248,700.00 USD annually

How we rate this

Applied Science Manager, Seller Partner Trust and Store Integrity Science - Global Risk Intelligence and Prevention at Amazon rates 84 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Key facts

Applied Science Manager, Seller Partner Trust and Store Integrity Science - Global Risk Intelligence and Prevention is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The listed salary is $184k-$249k a year.
The role is based in US, WA, Seattle.
The job was posted on October 12, 2026.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Computer visionNLP

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer vision and NLP. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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