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# Sr. Applied Scientist, Amazon Ads

- **Company:** Amazon.com, Inc.
- **Location:** Boston, MA, United States
- **Experience:** Expert
- **Salary:** $167,100.0 - $226,100.0
- **Contract:** Permanent contract
- **Skills:** Mxnet, Java (Programming Language), Computer Vision, Big Data, Business Software, C++ (Programming Language), Distributed Systems, R (Programming Language), Apache Hadoop, Python (Programming Language), Machine Learning, Natural Language Processing, NumPy, Recommender Systems, Software Tools, Tensorflow, SciPy, Large Language Models, Apache Spark, Deep Learning, Spark Mllib, Scikit Learn
- **Published:** July 25, 2026
- **Apply:** https://dejobs.org/x/x/B005CA718B7F4EF4A4725846F93BB941/job/

## About the Role

* 5+ years of building machine learning models for business application experience
 * PhD, or Master's degree and 6+ years of applied research experience
 * Experience programming in Java, C++, Python or related language
 * Experience with neural deep learning methods and machine learning

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.

## Description

As a Senior Applied Scientist in Amazon Ads, you will:

 * Research and implement cutting-edge ML approaches, including applications of generative AI and large language models
 * Develop and deploy innovative ML solutions spanning multiple disciplines - from ranking and personalization to natural language processing, computer vision, recommender systems, and large language models
 * Drive end-to-end projects that tackle ambiguous problems at massive scale, often working with petabytes of data
 * Build and optimize models that balance multiple stakeholder needs - helping customers discover relevant products while enabling advertisers to achieve their goals efficiently
 * Build ML models, perform proof-of-concept, experiment, optimize, and deploy your models into production, working closely with cross-functional teams including engineers, product managers, and other scientists
 * Design and run A/B experiments to validate hypotheses, gather insights from large-scale data analysis, and measure business impact
 * Develop scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience


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