Applied Scientist, Tabular Foundational Model

- 3+ 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
- 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.

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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 - 167,100.00 - 226,100.00 USD annually
Do you want to work on Tabular Foundational Model (TFM) to evolutionize how enterprises unlock insights from structured data? We're building Amazon's frontier large-scale tabular foundation model (Mitra family of models) that understands tables natively—enabling zero-shot analytics, automated feature engineering, and cross-domain generalization across diverse tabular datasets.

Come join the world class researchers and academics in the AWS AI endeavor, and develop the science that powers countless new businesses in cloud computing!

AWS, the world-leading provider of cloud services. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and journals.

The scientific topics you are going to work on include, but are not limited to: pre-training tabular foundational models (Mitra family of models) on heterogeneous tabular data, few-shot adaptation for domain-specific tasks (regression, classification), agentic feature engineering for handling high-cardinality features and missing data patterns, etc.