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.
Responsibilities
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.
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
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
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)