Sr. Applied Scientist, Infrastructure Reliability

Are you driven by innovation and complex problem-solving? At Infrastructure Reliability, we build scalable solutions that ensure the reliability of Amazon's critical systems. Our team develops and operates tools that detect and prevent outages to maintain high availability across Amazon's global infrastructure. Join us to architect solutions that directly impact millions of customers, with the resources and support to make meaningful contributions.

The team at Amazon is responsible for building intelligent and real-time insights into service-to-service communications, network traffic, and event correlation across hundreds of Amazon's critical fulfillment and robotics services. Our solutions support visibility into anomalous service behavior to prevent and quickly recover from incidents, ensuring high availability to keep the Customer Promise.

We are seeking a talented Senior Applied Scientist to invent the next generation of agentic observability solutions at Amazon scale. In this role, you will define, lead, and build the science behind intelligent systems that reason about complex network and infrastructure telemetry, autonomously detect anomalies, and drive automated remediation. You will own the scientific direction end to end, partnering closely with engineering, product, and Network Development Engineers to translate a long-term science vision into concrete research and delivery roadmaps.

Working backwards from the needs of our customers and operations teams, you will take the lead on ambiguous, high-impact problems where neither the problem nor the solution is well defined, and deliver production systems that improve infrastructure availability at scale. You will invent new methods, drive their adoption across multiple teams, and remain deeply hands-on with the hardest technical challenges.

Key job responsibilities
* Define and own the science vision for agentic observability, translating it into research and engineering roadmaps in partnership with product and engineering leaders.

* Build ML and agentic AI systems that autonomously detect, classify, and correlate infrastructure anomalies across network, compute, and service layers at Amazon scale.

* Design and develop models for event correlation, root cause analysis, and predictive failure detection using time-series analysis, graph-based methods, and deep learning.

* Own the agentic architecture for automated observability workflows, including planning, tool integration, long-horizon reasoning, and multi-agent orchestration.

* Define and curate the datasets and evaluation methodologies needed to train, benchmark, and continuously improve detection and classification systems.

* Stay deeply hands-on: write production-quality, critical-path code and build core components that take systems from prototype to launch.
Partner with Network Development Engineers and operations teams to ground science solutions in real-world infrastructure behavior and operational needs.

* Mentor scientists and engineers, raise the science bar, and represent the team in the internal and external scientific community through publications and presentations.

A day in the life
You will solve real-world problems by analyzing large-scale network telemetry and operational data, designing experiments and simulations, and developing ML models that detect and prevent infrastructure incidents. Your work requires close collaboration with engineers, Network Development Engineers, product managers, and operations leaders across the organization. You will prepare written and verbal presentations to share insights with audiences of varying technical sophistication, and you will iterate rapidly between research and production deployment.

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 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 in several of the following areas: machine learning, statistics, deep learning, natural language processing, or information retrieval
- Experience with time-series analysis, anomaly detection, or graph-based ML methods
- Experience with agentic AI architectures, LLMs, or multi-agent systems
- Experience applying ML to infrastructure, networking, or observability domains

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, TN, Nashville - 158,800.00 - 214,800.00 USD annually