Applied Scientist II, Search Ranking, Search Ranking

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. In this role, you will design and post-train deep ranking models, including LLM-based rankers and multi-tower deep learning models, that jointly optimize purchase, relevance, and personalization. You will 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.

Personalization is a first-class objective on this team. You will 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.

We treat search as an active research frontier and invest heavily in staying at the leading edge of ML. Beyond today's ranking stack, our current explorations include LLM agents that reason and plan across multi-step workflows, tool-augmented foundation models, and new paradigms that combine retrieval, reasoning, and personalization. You will help chart where search goes next, and see your ideas ship to real customers within weeks, not quarters.

You will work in a dynamic, entrepreneurial team while leveraging the resources of , one of the world's leading technology companies. Please visit https://(Use the "Apply for this Job" box below). for more information.

Your responsibilities include but are not limited to:

• 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. Lambda Loss, List Net, 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.

• 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 life cycles, and optimization of model execution, or experience leading and influencing your team or organization
• Experience with learning-to-rank and listwise ranking losses (Lambda Loss, Lambda Rank, List Net, 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, WSDM).

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Los Angeles County applicants:
Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position.

These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and…