Amazon Development Center U.S., Inc.

Technology

SeniorAppliedScientist,AgenticWorkSpaces

$100–226k Seattle, Washington, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Applied Scientist, Agentic WorkSpaces at Amazon Development Center U.S., Inc.. Skills: Machine learning, Capacity management, Demand forecasting, Resource optimization. Architect ML foundations. Implement ML foundations”

What You'll Achieve.

ML systems enable profitability; Reduce capacity waste; Improve cost efficiency; Eliminate customer-impacting events; Unlock significant cost savings; Maintain safety margins; Enable autonomous decision-making

Industry & Context.

Technology
Problems you'll solve

Ambiguous problems; Complex business requirements; Capacity risks; Demand volatility

What They're Looking For.

Must Have

3+ years building ML models, PhD or Master's degree, 6+ years applied research experience, Programming in Java, C++, Python, Experience with neural deep learning, Experience with machine learning

Nice to Have

Experience with R, Experience with scikit-learn, Experience with Spark MLLib, Experience with MxNet, Experience with Tensorflow, Experience with numpy, Experience with scipy

What You'll Do.

Architect ML foundations

Implement ML foundations

Build forecasting systems

Build anomaly detection systems

Design optimization algorithms

Partner with product teams

Translate product vision

Work on capacity forecasting problems

Identify research challenges

Deliver with limited guidance

Drive alignment on ML approaches

Drive alignment on capacity strategies

Develop end-to-end solutions

Deliver end-to-end solutions

Make technical trade-offs

Lead on business problems

Shape ML research direction

Propose new approaches

Set example with solutions

Set example with code

Set example with designs

Set example with artifacts

How You'll Work.

Team & Collaboration

Product and engineering teams; Product managers; Business stakeholders; Engineering teams; Operations teams

Full Job Description

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 resource utilization and placement strategies. • Apply advanced ML techniques including time-series forecasting, reinforcement learning, and causal inference to measure th

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