Sr. Applied Science, Agentic WorkSpaces (AAWS)

About the role

AWS Applied AI Solutions (AAIS) is where science meets customer obsession at scale. We build the intelligent systems that power AWS services used by millions, combining research in machine learning, agentic AI, and applied science with the operational rigor required to deliver enterprise‑grade experiences. Within AAIS, Amazon WorkSpaces is our cloud‑based virtual desktop service that delivers secure, managed computing to over one million daily users worldwide, enabling organizations to provision, manage, and scale desktops with the reliability and performance their workforce depends on. The Senior Applied Scientist will own and advance the science behind capacity modelling for Amazon WorkSpaces, designing, building, and continuously improving forecasting and optimization models that ensure the right compute, storage, and networking resources are available at the right time, in the right regions, at the lowest possible cost, without compromising the end‑user experience.

Responsibilities

• Define and drive the scientific strategy for capacity modelling, establishing the research agenda that transforms how WorkSpaces forecasts demand, plans supply, and allocates resources across a globally distributed infrastructure.
• Build advanced demand forecasting models that predict workspace usage across multiple time horizons, from intraday spikes to long‑range growth trajectories, incorporating signals such as customer onboarding patterns, seasonal trends, regional expansion, and macroeconomic indicators.
• Design supply optimization frameworks that determine optimal resource placement, instance mix, and pre‑warming strategies, balancing availability, performance, and cost by reasoning over hardware constraints, pricing dynamics, and service‑level objectives.
• Develop causal and probabilistic models that move beyond trend extrapolation to true understanding of demand drivers, enabling the organization to distinguish organic growth from one‑time events, anticipate shifts in usage patterns, and quantify uncertainty in planning decisions.
• Architect simulation and scenario‑planning systems that allow business and engineering leaders to run “what‑if” analyses, stress‑test capacity plans against disruption scenarios, and evaluate trade‑offs between investment timing, risk tolerance, and customer experience.
• Pioneer the integration of machine learning with operations research, combining deep‑learning‑based forecasting with mathematical optimization to jointly solve the demand prediction and resource allocation problem.
• Establish evaluation frameworks and monitoring systems that measure forecast accuracy, capacity utilization, and cost efficiency in production, creating tight feedback loops that drive continuous model improvement and build organizational trust in science‑driven planning.
• Influence the broader organization’s capacity strategy by translating model outputs into actionable recommendations for leadership, identifying opportunities to extend capacity intelligence patterns to adjacent services, and mentoring scientists and engineers across the team.

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 a 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.

Compensation and Benefits

The base salary range for this position in Seattle, WA is $167,100 – $226,100 USD annually. The Amazon package includes sign‑on payments and restricted stock units (RSUs). Final compensation is determined based on experience, qualifications, and location.

Amazon offers comprehensive benefits including health insurance (medical, dental, vision, prescription), Basic Life & AD&D insurance with optional supplemental life plans, Employee Assistance Program, mental‑health support, medical advice line, flexible spending accounts, adoption and surrogacy reimbursement, 401(k) matching, paid time off, and parental leave.

Location

Seattle, Washington, USA.