Amazon
Senior Applied Scientist, Frontier AI Tooling
$167K–$249K
Austin, TX +3 · Senior · Full-time · Posted Oct 13, 2026
- Location
- Austin, TX
Arlington, VA
Seattle, WA
New York, NY - Experience
- 6+ years with a Master’s degree; PhD is an alternative. Preferred research alternatives include a PhD or equivalent research experience, or a Master’s degree and experience with patents or publications at top-tier peer-reviewed conferences or journals.
- Education
- PhD, or Master’s degree and 6+ years of relevant experience.
About the job
Build and deploy machine-learning and AI tools that help AWS Sales and Delivery teams understand customer needs and deliver solutions, while setting scientific direction and mentoring colleagues.
What you'll do
- Own complex science problems end to end, from framing and modeling through evaluation and production deployment.
- Partner with business stakeholders to translate open-ended questions into science and actionable recommendations.
- Set technical direction, communicate results, and raise the team’s scientific bar through reviews and mentorship.
What they ask for
- PhD, or Master’s degree and 6+ years of experience building machine-learning models for business applications.
- Experience with SQL and Python scripting.
- Experience communicating complex technical information clearly to executives and non-technical leaders.
- Experience managing and deploying ML products.
Nice to have
- Experience using financial models, KPIs, and data analysis to inform business decisions with demonstrated impact.
- Depth in large language models and agentic systems; experience mentoring scientists or qualifying research credentials is also noted.
Pay
USD 183,800.00 - 248,700.00 annually base salary (New York); USD 167,100.00 - 226,100.00 annually base salary (Austin); USD 167,100.00 - 226,100.00 annually base salary (Arlington); USD 167,100.00 - 226,100.00 annually base salary (Seattle)
Tools
Python, PySpark, SQL
AI focus
Develop GenAI agents, knowledge graphs, recommendation systems, synthetic controls, account prioritization models, and a context layer to improve agent quality, accuracy, reliability, and security.
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