Job description

In this role, you will design and build intelligent multi-agent systems that automate root cause analysis for advertising campaign delivery at scale. You will architect agentic orchestration patterns where specialized sub-agents (campaign diagnostics, deal-level troubleshooting, pacing control) are invoked as composable tools by a reasoning layer that determines which subsystems to query based on the nature of the issue. You will develop hierarchical analysis frameworks that move from daily trend detection to intra-day anomaly isolation, enabling the system to pinpoint when and why delivery degraded rather than relying on static time windows. You will build self-learning feedback loops where the system identifies recurring failure signatures (auction dynamics, pacing anomalies, supply contention), updates its diagnostic knowledge as engineering teams deploy fixes, and retires stale patterns automatically. We are looking for a passionate Applied Scientist with technical expertise in LLM-based agent architectures, retrieval-augmented generation, time-series anomaly detection, and production ML systems. In addition to hands-on experience building agentic AI solutions, an ideal candidate should demonstrate the ability to translate complex distributed system behaviors into structured diagnostic reasoning, show a willingness to push the boundaries of how LLMs interact with real-time operational data, and thrive in an environment where you ship production systems that directly reduce advertiser escalation time from days to minutes. Key job responsibilities * 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. A day in the life Your primary focus is building a multi-agent diagnostic system that automates root cause analysis for advertising campaign delivery issues. On a typical day, you might review how the system handled recent escalations, identify where it reasoned incorrectly, adjust orchestration logic, and write new evaluation cases. You will design agent architectures that invoke specialized sub-agents as tools, build hierarchical analysis frameworks that move from trend detection to anomaly isolation, and develop self-learning loops that keep the system's diagnostic knowledge current as the underlying platform evolves. You will work closely with SDEs building the diagnostic platform, product managers defining the troubleshooting experience, and the support teams who rely on your system to resolve advertiser delivery issues in minutes instead of days. Beyond the core agent work, you may find yourself diving into causal inference to measure recommendation effectiveness, prototyping proactive anomaly detection, or contributing to evaluation science for systems that reason over complex operational data. About the team The Demand Enablement, Product Analytics and Operations team builds the diagnostic and intelligence layer for Amazon DSP, the demand-side platform powering Amazon's programmatic advertising business. We own the systems that detect, diagnose, and surface delivery issues across campaigns, giving internal teams and advertisers the visibility to act before problems impact spend. Our product portfolio spans automated troubleshooting platforms, advertiser-facing delivery insights, and AI-powered root cause analysis using multi-agent architectures on foundation models. We are a small, high-ownership team that ships production systems end-to-end, from data pipelines processing billions of bid events to LLM-based agents that reason over complex advertising systems. If you want to work at the intersection of applied science, distributed systems observability, and real business impact measured in advertiser dollars recovered, this is the team. - 3+ years of building models for business application experience - 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 any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing - Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms - Experience in professional software development - Experience in designing experiments and statistical analysis of results 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 amazon.jobs 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 amazon.jobs . USA, NY, New York - 172,400.00 - 223,400.00 USD annually USA, WA, SEATTLE - 142,800.00 - 193,200.00 USD annually

Requirements

Must-haves

Key job responsibilities
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.
A day in the life
About the team
3+ years of building models for business application experience
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 any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
Experience in professional software development
Experience in designing experiments and statistical analysis of results
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
USA, NY, New York - 172,400.00 - 223,400.00 USD annually
USA, WA, SEATTLE - 142,800.00 - 193,200.00 USD annually

Nice-to-haves

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Application prep

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Keywords to mirror

llmmlaimachine learningjavapython

Platform: amazon
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Paid Time Off

Parental Leave