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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
Job Responsibility:
Lead the research and development of machine learning and statistical models that power Alexa's reliability at scale
Work on complex problems from time series multivariate modeling, statistical anomaly detection to LLM-based operational intelligence and adaptive threshold systems
Design and implement production-grade ML solutions and establish rigorous model evaluation frameworks
Ensure LLM-powered systems are grounded, reliable, and free from systematic bias using techniques such as RAG, confidence scoring, knowledge graph integration, and counterfactual testing
Partner with software engineers, product managers, and operations leaders to translate scientific innovation into production systems
Drive the scientific agenda for the team, mentor fellow scientists, and influence the broader organization through technical leadership and cross-team collaboration
Requirements:
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
Nice to have:
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.