Applied Scientist II, Search Ranking, Search Ranking

Socket.dev; Seattle, Northern (WA, KY); Hybrid; USD 143,000 - 193,000; Full time

Job summary

Amazon Search team builds high-performance distributed search systems ranking billions of products for shoppers worldwide.

You will design post-trained deep ranking models, including LLM-based rankers, to optimize engagement, relevance, and personalization. Work spans research prototypes to production experiments in a fast-paced, scalable environment.

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 algorithms, data structures, parsing, numerical optimization, data mining, parallel and distributed computing, HPC

Responsibilities

• Design and deploy state-of-the-art ranking models for Amazon search
• Post-train LLMs and ranking models with supervised fine-tuning and RL
• Compose multi-objective ranking approaches balancing engagement, relevance, and personalization
• Design large-scale label pipelines turning signals into training and reward signals
• Evaluate solutions via offline benchmarks and online A/B tests
• Publish and present work at ML/NLP/IR venues

Skills

Java

C++

Python

Distributed computing

Machine Learning

Education

PhD or MS + 4+ years

Tools

PyTorch

JIT / AOT

Job description

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 Amazon.com, one of the world's leading technology companies. Please visit https://www.amazon.science for more information.

Key job responsibilities

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. 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.

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).

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 respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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 https://amazon.jobs/content/en/how-we-hire/accommodations 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 https://amazon.jobs/en/benefits.

USA, CA, Palo Alto - 171,600.00 - 222,200.00 USD annually

USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually