Applied Scientist II: AI Diagnostic Systems for Ad Delivery

Amazon; New York (NY); Vor Ort; USD 172.000 - 223.000; Vollzeit

Zusammenfassung

Amazon.com Services LLC is seeking an Applied Scientist to design and build intelligent multi-agent systems for automated root cause analysis of advertising campaign delivery at scale. You will architect agentic orchestration patterns and hierarchies of subsystems, enabling rapid isolation of delivery issues and continuous self‑learning of diagnostic knowledge.

The role emphasizes production ML, retrieval‑augmented generation, and real‑time data interaction.

Qualifikationen

• 3+ years building models for business applications.
• PhD, or Master’s degree and 4+ years of CS, CE, ML or related field experience.
• Experience programming in Java, C++, Python or related language.
• Experience in algorithms, data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.

Aufgaben

• Conduct deep data analysis to derive insights for the business, identify gaps, and uncover new opportunities.
• Develop scalable and effective machine learning models and optimization strategies to solve business problems.
• Run regular A/B experiments, gather data, and perform statistical analysis to optimize advertiser experiences.
• Collaborate closely with software engineers to deliver end-to-end solutions into production.
• Enhance the scalability, efficiency, and automation of large-scale data analytics, model training, deployment, and serving.
• Research and implement new machine learning models and techniques to improve advertising performance.

Kenntnisse

Java

C++

Python

Algorithms

Data structures

Ausbildung

PhD or MS + 4+ years CS/ML

Jobbeschreibung

Amazon.com Services LLC is seeking an Applied Scientist to design and build intelligent multi-agent systems for automated root cause analysis of advertising campaign delivery at scale. You will architect agentic orchestration patterns and hierarchies of subsystems, enabling rapid isolation of delivery issues and continuous self‑learning of diagnostic knowledge.

The role emphasizes production ML, retrieval‑augmented generation, and real‑time data interaction.