# Sr. Applied Scientist, Sales AI at Amazon

Amazon is hiring a Sr. Applied Scientist, Sales AI in Sunnyvale, United States. It pays $192k-$260k a year and Level rates it Builds AI ●●●●; you can [apply on Amazon](https://jobsbylevel.com/go/88195b05-9bb7-4649-bd54-a00d338518a9?utm_medium=ai-agent).

AI Level 4, AI centrality 91 out of 100. US, CA, Sunnyvale.

## Details

- Company: [Amazon](https://jobsbylevel.com/companies/amazon)
- AI level: AI Level 4 (score 91 out of 100)
- Location: US, CA, Sunnyvale
- Salary: $192k-$260k
- Posted: November 13, 2026
- Apply: https://jobsbylevel.com/go/88195b05-9bb7-4649-bd54-a00d338518a9?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=sr-applied-scientist-sales-ai-at-amazon-1c661e

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/sr-applied-scientist-sales-ai-at-amazon-1c661e

## Description

Love using AI to solve big, messy problems that actually move a business? Join Sales AI as an Applied Scientist and own the science behind how Amazon Ads helps every advertiser grow, from understanding where each brand is on its growth journey to building the GenAI agents account teams use every day. Advertising is one of the fastest growing parts of our business, and science sits at the heart of how we help brands succeed. Our scientists work on two connected problems. The first is figuring out, for each brand, the smartest next step it can take to grow, and turning that into a clear, personalized recommendation. The second is building LLM-powered assistants and agents that put that intelligence into the hands of the people who work with those brands, accurately and at scale. If you want real ownership, hard problems, and models that ship, we'd love to talk. Key job responsibilities You'll own production science systems that account teams rely on every day. For example, you might build the recommendation engine that maps where each advertiser is in their growth journey and what their best next step is, or improve the accuracy of the LLM-powered assistants and agents that account teams use to prepare for and run customer conversations. Day to day that means: - Turn ambiguous business problems into well-defined science problems, and choose the right approach to solve them - Build, ship, and continuously improve models in production, and own their quality once they launch - Measure whether our models really move the business, through A/B tests, causal analysis, and offline evaluation - Partner with engineers and product managers to take ideas from early research into production - Set the technical direction for your area, raise the scientific bar, and mentor other scientists - Explain complex methods and trade-offs clearly to engineers, product partners, and sales leaders A day in the life No two days look the same. You might start by testing a new modeling approach, then review evaluation results with engineers to decide whether a model is ready to launch. Later you could meet with product partners to agree on what to build next, or dig into data to understand why a recommendation isn't landing. Your customers are the account teams who work directly with brands, and the brands themselves, who rely on your work to decide where to invest and how to grow. About the team Sales AI is a central science and data engineering organization within Amazon Advertising Sales. Our mission is to power selling motions and account team workflows with state-of-the-art AI and ML services. We build advertiser insights, recommendations, and generative AI-powered applications woven throughout account team workflows. We care about a high scientific bar, real business impact, and taking ideas all the way into production. Most of all, we like working with curious people who want to drive real, lasting impact for our customers. Basic qualifications - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience in building machine learning models for business application - Experience with one of the following areas: machine learning technologies, Reinforcement Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP) or related applications Preferred qualifications - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. - Experience with neural deep learning methods and machine learning - Applied research experience in Reinforcement Learning (RLHF, policy gradient methods), Large Language Models (fine-tuning, guardrails, distributed training) and/or Natural Language Processing Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected

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