Senior Applied Scientist, Agentic WorkSpaces
Amazon - Seattle, Washington, USA
Posted Apr 10, 2026
Benefits
- Parental leave
- 6 weeks From the posting source checked Jun 20, 2026
- Non-birth-parent leave
- 6 weeks From the posting source checked Jun 20, 2026
- Family-building benefits
- Mental health support
- Offered From the posting source checked Jun 20, 2026
- Relocation assistance
- Not verified
- Childcare support
- Offered From the posting source checked Jun 20, 2026
- Learning budget
- Not verified
- Verification
- Source-linked checked Jun 7, 2026
- Salary
- $167K-$226K From the posting source checked Jun 20, 2026
- 401(k) match
- Reported from DOL Form 5500 industry filing (not employer-specific)
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Market context
- U.S. role benchmark (BLS OEWS)
- $111,944 U.S. median for this role
- Projected growth (BLS Employment Projections)
- +13.7% - Much faster than average
76% above the BLS role benchmark for data and ml aggregate.
Matched to SOC 15-1252 - Data and ML aggregate by role bucket.
Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.
Role
Schedule
- Shift type
- Not verified
- Weekend work
- Not verified
Company
Application
- Cover letter
- Not verified
- Assessment
- Not verified
- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Senior Applied Scientist, Agentic WorkSpaces Seattle, Washington, USA About the Organization AWS is on a mission to transform how businesses operate by delivering intelligent, cloud-powered applications. Our Applied AI Solutions organization accelerates customer success through intuitive, differentiated technology that solves enduring business challenges - blending vision with real-world expertise to build turnkey solutions that are easy to adopt and built to scale. Within this organization, we are building the next generation of secure, intelligent workspaces - environments purpose-built for human-AI collaboration at enterprise scale. The Role We are looking for a Senior Applied Scientist to build the predictive intelligence powering capacity management for our workspace platform - developing machine learning systems that forecast demand, optimize resource allocation, and enable cost-efficient scaling at massive scale. This role requires someone who can translate complex business requirements into production ML systems, designing algorithms that balance customer experience with operational efficiency across a large and diverse fleet of capacity pools. What You'll Do • Architect and implement ML foundations for capacity management, building models that continuously learn and optimize across multiple dimensions including geography, platform, and instance type. • Develop demand forecasting systems that anticipate usage patterns hours to weeks in advance, enabling proactive capacity decisions at scale. • Build anomaly detection systems that identify capacity risks before they impact customers, improving service reliability and resilience. • Design optimization algorithms that make high-frequency, automated decisions balancing two critical forces: ensuring a flawless customer experience where every operation succeeds, while maximizing cost efficiency through intelligent
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