Amazon's PRISM team is seeking an innovative Applied Scientist to build the intelligence layer powering the Catalog Diagnostic Assistant - a conversational AI agent that unifies Amazon's fragmented catalog diagnostic experience into a single natural-language interface. Rather than switching between legacy tools, internal users ask CDA a question in plain language and CDA coordinates across data sources, reasons through complex diagnostic workflows, and returns a combined answer with source citations.
This role sits at the intersection of Generative AI, agentic architectures, and large-scale information retrieval applied to the world's largest product catalog. You will design and build the scientific core of an agent that autonomously investigates catalog anomalies - diagnosing why products aren't live, why attributes aren't publishing, or why matching decisions went wrong - across billions of products, petabytes of multimodal data, and dozens of marketplaces.
You will be the founding scientist for the CDA product, defining the research agenda for agentic diagnostics, developing novel approaches to skill-based reasoning and tool orchestration, and owning the full lifecycle from problem formulation through production deployment at Amazon scale. You will pioneer advanced GenAI solutions that power next-generation agentic experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog and tackle problems at the frontier of AI research.
Key job responsibilities
- Formulate open research problems at the intersection of GenAI, agentic reasoning, and large-scale catalog diagnostics - defining how an autonomous agent should decompose, investigate, and explain complex catalog issues
- Design and develop novel agentic architectures (skill planning, tool selection, multi-step reasoning, chain-of-thought verification) that enable CDA to autonomously resolve diagnostic workflows that traditionally required manual expert investigation
- Build and optimize retrieval-augmented generation (RAG) systems over Amazon's catalog data, ensuring the agent retrieves the right evidence from the right data sources to ground its diagnostic answers
- Advance the science of efficient model deployment - developing distillation, compression, and LLM serving optimization strategies that preserve diagnostic reasoning quality in production-grade architectures while reducing latency and cost
- Make frontier models reliable for autonomous decisions - advancing uncertainty calibration, confidence estimation, and interpretability methods so CDA's agentic diagnoses can be trusted at scale
- Own the full research lifecycle from problem formulation through production deployment - designing rigorous experiments, iterating rapidly, and seeing your research directly improve diagnostic accuracy and coverage
- Partner closely with CDA engineers to translate research prototypes into production systems serving thousands of daily diagnostic sessions
- Shape the team's research vision by defining technical roadmaps that balance foundational scientific inquiry with measurable product impact
- Mentor engineers on advanced ML/GenAI techniques, experimental design, and scientific rigor
BASIC QUALIFICATIONS
- 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
- 2+ years of building machine learning models or developing algorithms for business application experience
PREFERRED QUALIFICATIONS
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Experience with explainable machine learning and artificial intelligence methodologies and tools
- Experience in written and verbal communication with the ability to present complex technical information in a clear and concise manner to executives and non-technical leaders
- Strong experimental design skills and statistical analysis expertise
- Experience with LLMs, VLMs, foundation models, or large-scale deep learning systems-including multimodal pretraining, fine-tuning, RLHF, prompt engineering, or agentic architectures
- Track record of deploying ML models at scale in production environments processing billions of data points
- Publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, EMNLP, ACL, NAACL, COLING, KDD, SIGMOD, WWW, AAAI, or similar
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, CA, Sunnyvale - 171,600.00 - 222,200.00 USD annually
