Applied Scientist II, Search Ranking, Search Ranking

About the role

The Amazon Search team creates customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Product Search services go to work. We design, develop, and deploy high-performance distributed search systems that rank a catalog of billions of products for hundreds of millions of shoppers. The Search Relevance team owns the ranking models that decide the order of results on every Amazon search page.

What you'll do

• Design, train, and deploy state-of-the-art ranking models that decide how results are ordered on Amazon search, spanning LLM-based rankers and multi-tower deep learning architectures that jointly model engagement, relevance, and personalization
• Post-train LLMs and ranking models with supervised fine-tuning, reinforcement learning (e.g. GRPO, DPO, RLHF), knowledge distillation, and listwise ranking losses (e.g. LambdaLoss, ListNet, ListMLE)
• Compose multiple objectives (engagement, relevance, personalization) into a single ranking through principled multi-objective optimization at inference
• Design large-scale label pipelines, including LLM-as-teacher supervision, that turn customer signals and expert judgment into training and reward signals
• Optimize inference for production ranking models through quantization, quantization-aware training, teacher-student distillation, and serving-stack tuning
• Evaluate proposed solutions through offline benchmarks and online A/B tests, and drive the analysis that decides whether a change ships
• Publish and present your work at internal and external scientific venues in ML, NLP, and IR
• Invent modeling and training techniques that push the Pareto frontier across multiple objectives, and take your work end to end from novel research prototype through offline evaluation to production online experimentation
• Build models that reason over each customer's history, durable preferences, and query intent to decide which results best fit that specific customer, rather than optimizing a single population-level ranking
• Help chart where search goes next, including explorations of LLM agents that reason and plan across multi-step workflows, tool-augmented foundation models, and new paradigms that combine retrieval, reasoning, and personalization

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
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred qualifications

• Experience with PyTorch, JIT compilation, and AOT tracing, or experience with vLLM, SGLang, TensorRT or similar platforms in production environments
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience leading and influencing your team or organization
• Experience with learning-to-rank and listwise ranking losses (LambdaLoss, LambdaRank, ListNet, ListMLE, ApproxNDCG)
• Experience designing large-scale online A/B tests and analyzing offline-to-online metric correlation
• Publications in top ML, NLP, or IR venues (such as NeurIPS, ICML, ACL, EMNLP, SIGIR, KDD, WWW, WSDM)

Pay and benefits

• Base salary range: USA, CA, Palo Alto - $171,600.00 - $222,200.00 USD annually; USA, WA, Seattle - $142,800.00 - $193,200.00 USD annually
• Package includes sign-on payments and restricted stock units (RSUs)
• Final compensation determined based on factors including experience, qualifications, and location
• 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

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