Sr. Applied Scientist, Alexa Excellence AI Ops, Alexa Excellence AI Ops

Amazon.com Services LLC; Sunnyvale (CA); On-site; USD 190,000 - 240,000; Full time

Job summary

Amazon.com Services LLC in Sunnyvale seeks a Senior Applied Scientist to lead ML research for Alexa Availability, powering reliability at massive scale for hundreds of millions of customers.

You will tackle time-series modeling, anomaly detection, LLM-based operational intelligence, and adaptive thresholds, delivering production-grade ML solutions with rigorous evaluation and bias mitigation.

Qualifications

• PhD or Master's degree with 6+ years of applied research experience.
• 3+ years building ML models for business applications.
• Experience programming in Java, C++, Python or related language and familiarity with neural deep learning methods.

Responsibilities

• Lead research and development of ML and statistical models for Alexa Availability at scale.
• Design production-grade ML solutions and rigorous evaluation frameworks; ensure unbiased, reliable AI systems.
• Collaborate with engineers, PMs, and operations leaders to translate science into production systems with global impact.

Skills

Java/C++/Python

ML modeling

Neural networks

Education

PhD or MS + 6+ years

Tools

R

scikit-learn

Spark MLLib

MxNet

TensorFlow

NumPy

SciPy

Job description

We are seeking a Senior Applied Scientist to join the Alexa Availability team within Alexa Excellence. This role leads the research and development of machine learning and statistical models that power Alexa's reliability at massive scale - serving hundreds of millions of customers globally.

The ideal candidate will tackle complex, ambiguous problems spanning time series multivariate modeling, statistical anomaly detection, LLM-based operational intelligence, and adaptive threshold systems. They will design production-grade ML solutions, establish rigorous evaluation frameworks, and ensure AI systems are grounded, reliable, and free from systematic bias - leveraging techniques such as RAG, confidence scoring, knowledge graph integration, and counterfactual testing.

This scientist will partner with engineers, product managers, and operations leaders to translate scientific innovation into production systems that directly impact Alexa's availability worldwide. They will drive the scientific agenda for the team, mentor fellow scientists, and influence the broader Alexa Excellence organization through technical leadership and cross-team collaboration.

Key Focus Areas

• Anomaly detection and predictive failure modeling
• Cross-service correlation and LLM-driven operational intelligence
• Production ML at the intersection of large-scale distributed systems and applied science
• Model reliability, hallucination mitigation, and grounding for operational AI

Key job responsibilities

As a Senior Applied Scientist on the Alexa Availability team, you will lead the research and development of machine learning and statistical models that power Alexa's reliability at scale. You will work on some of the most complex and ambiguous problems in the space - from time series multivariate modeling and statistical anomaly detection to LLM-based operational intelligence and adaptive threshold systems.

A day in the life

You will design and implement production-grade ML solutions, establish rigorous model evaluation frameworks, and ensure our LLM-powered systems are grounded, reliable, and free from systematic bias. You will apply techniques such as Retrieval-Augmented Generation (RAG), confidence scoring, knowledge graph integration, and counterfactual testing to ensure our AI systems make trustworthy operational decisions at scale.

You will partner closely with software engineers, product managers, and operations leaders to translate scientific innovation into production systems that directly impact Alexa's availability for customers worldwide. You will drive the scientific agenda for your team, mentor fellow scientists, and influence the broader Alexa Excellence organization through your technical leadership and cross-team collaboration.

About the team

The Alexa Excellence team is at the heart of delivering a world-class Alexa experience to hundreds of millions of customers globally. Within Alexa Excellence, the Alexa Availability team is responsible for ensuring Alexa is always on, always responsive, and always reliable. We own the systems, signals, and science that detect, diagnose, and drive resolution of availability issues at scale - before customers ever notice.

We are building the next generation of intelligent availability solutions powered by machine learning, large language models, and advanced statistical modeling. Our work spans anomaly detection, predictive failure modeling, cross-service correlation, and LLM-driven operational intelligence - all operating at the scale and reliability bar that Alexa demands. We operate at the intersection of large-scale distributed systems, applied machine learning, and operational excellence, and we are looking for scientists who can bring both deep technical rigor and a bias for production impact.

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 with large scale distributed systems such as Hadoop, Spark etc.

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

Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These