Six Figure Work

Applied Scientist, Tabular Foundational Model, AWS

Amazon

Company websiteamazon.com (Amazon, opens in new tab)

Base salary $167,100  to $226,100

Employer-published annual base salary, USD. Advertised base range in Seattle, WA

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  • LocationSeattle, WA
  • WorkplaceWorkplace not stated
  • Status at last checkSource accepting applications when checked
  • Last checked (6 h ago) · observed since

About this job

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.

Basic Qualifications

  • 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

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.

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 - 167,100.00 - 226,100.00 USD annually

Source: Amazon · View original posting (opens in new tab)

Pay in context

This role's base floor ($167,100) is above 64% of the 329 Data & ML roles in Washington listed on this site; 32 others list the same floor. Their median floor is $143,700; the middle half runs from $136,000 to $168,000.

Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 7, 2026.

This range next to 6 other live Amazon roles with the nearest advertised base floor
Chart as a table
This range next to 6 other live Amazon roles with the nearest advertised base floor
RolePublished base
Applied Scientist, Tabular Foundational Model, AWS (this listing)$167,100 - $226,100
Software Development Engineer, Neuron Collectives, Annapurna Labs$165,200 - $223,600
Software Development Engineer -Control Plane, AWS Scalable Intent Driven Routing (SIDR)$165,200 - $223,600
Software Engineer II - AI/ML, Neuron Inference, Neuron Inference$165,200 - $223,600
Software Dev Engineer II - AMZ27744.1$165,200 - $223,600
Machine Learning Engineer, FAI$165,200 - $223,600
Software Development Engineer, AI/ML, AWS Neuron, Model Inference$165,200 - $223,600

Same $100K to $400K scale as every card. All Amazon roles · How to read the bar

Observed changes

  1. First observed on the employer's board
  2. Began meeting the published-salary criteria
Observed since
Employer-stated posting date
2026-09-25
Where this pay comes from

Location as the employer wrote it: US, WA, Seattle

USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually

Method
Employer text, read with fixed rules and re-verified
Checked
Rule set
us-base-v2

The pay passage appears in the employer's own job text. Fixed, deterministic rules located it, and the passage below was checked against the stored copy of the posting.

What we verified

  • The pay figures come from the employer's own listing, quoted exactly above.
  • The pay is an annual base salary in US dollars with a minimum of at least $100,000.
  • The role was publicly listed on the employer's job board when last checked.

We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.