Senior AI Scientist - Frontier AI Tooling & GenAI

Amazon Science; Austin (TX); On-site; USD 167,000 - 226,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Job summary

Amazon Science seeks a Senior Applied Scientist to drive end-to-end ML solutions for Frontier AI and Proserve teams, delivering scalable outcomes for customers. You will own high-impact problems from framing to deployment, partnering with AWS Sales to define requirements and deliver trusted results.

You will lead with rigorous quantitative methods, mentor peers, and set technical direction where no clear approach exists, using Python, SQL, and PySpark to build robust models and production

Qualifications

• PhD or Master's with 6+ years in ML for business applications.
• Experience with SQL and Python scripting.
• Strong communication to executives and non-technical leaders.
• Experience managing and deploying ML products.

Responsibilities

• Own the full lifecycle of complex science problems: framing, data exploration, method selection, modeling, evaluation, and production deployment.
• Set technical direction on ambiguous problems and make design decisions others build on.
• Partner with business stakeholders to translate open-ended questions into well-scoped science and actionable recommendations.
• Raise the scientific bar through design reviews, mentorship, and hands-on guidance.
• Communicate methods, trade-offs, and results clearly to technical and non-technical audiences.
• Use Python, PySpark, and SQL for data analysis and model development.

Skills

Machine learning

Model deployment

Communication with leadership

Education

PhD or Master's degree

Tools

SQL

Python

Job description

Amazon Science seeks a Senior Applied Scientist to drive end-to-end ML solutions for Frontier AI and Proserve teams, delivering scalable outcomes for customers. You will own high-impact problems from framing to deployment, partnering with AWS Sales to define requirements and deliver trusted results.

You will lead with rigorous quantitative methods, mentor peers, and set technical direction where no clear approach exists, using Python, SQL, and PySpark to build robust models and production