Applied Scientist II, Alexa for Shopping

Description

We are looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology powering Alexa for Shopping, our AI-driven search and shopping assistant, helping customers with their shopping tasks at every step of their shopping journey.

Key job responsibilities

As an Applied Scientist on our team, you will develop and maintain LLM agents, including automated eval pipelines, LLM-as-a-judge methodologies, rubric design, and dataset curation to measure nuanced aspects of response quality. You will partner with the wider org to experiment with techniques such as retrieval augmentation, context enrichment, prompt decomposition, and model fine-tuning or post-training strategies, if and when applicable. Where latency and cost constraints demand it, you will lead post-training of small language models (SLMs) - including supervised fine-tuning, preference optimisation, and distillation - to deliver low-latency conversational and shopping experiences. You will apply applied machine learning and deep learning techniques as last-mile improvements to shopping experiences, that might span ranking, relevance, personalisation, and multimodal understanding. You will design and evaluate agentic architectures that balance the needs of diverse shopping use cases, making principled choices across paradigms such as single-agent and multi-agent systems, memory management strategies, and tool orchestration to optimise for quality, latency, and reliability at scale. You will leverage petabytes of data and identify opportunities to leverage machine learning models aimed at making conversational systems more performant.

A day in the life

Perform hands-on analysis of large-scale multimodal interaction datasets to develop insights into how customers engage with conversational AI systems and how to improve response quality and customer experience. Use statistical methods, experimentation, and data-driven analysis to develop scalable approaches for measuring, evaluating, and optimizing large language model (LLM)-based shopping assistant systems, leveraging structured and unstructured contextual signals. Conduct deep-dive analyses to identify opportunities for improving conversational relevance, grounding, customer satisfaction, and downstream business impact. Collaborate with Product management and Engineers to translate analytical insights into production systems, working closely on model evaluation and deployment. Communicate results and insights to both technical and non-technical audiences, including through presentations, written reports, and data visualizations.

About The Team

The Alexa for Shopping Science team, based in London, works alongside ~150 engineers, designers and product managers, shaping the future of AI-driven shopping experiences at Amazon. The team works on every aspect of the conversational AI system, from making it agentic, enabling customers to set price alerts or empower the assistant to act on their behalf and automatically purchase products when the price is right, to understanding multimodal user queries and generating answers that combine text, image, audio and video, including deep research reports that scour the web and the Amazon catalog to provide detailed and personalised shopping guidance. We utilize and advance state-of-art techniques in the fields of Natural Language Processing, gen AI, Information Retrieval, Machine/Deep Learning, and Data Mining. We validate our work by actively participating in the internal and external scientific communities.

Basic Qualifications

• Master's degree or above in computer science, machine learning, engineering, or related fields
• Experience programming in Java, C++, Python or related language
• Experience building machine learning models or developing algorithms for business application
• Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• Experience in patents or publications at top-tier peer-reviewed conferences or journals

Preferred Qualifications

• PhD in computer science, machine learning, engineering, or related fields
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in debugging, profiling, and implementing software engineering best practices in large-scale systems
• Strong publication records in top-tier NLP/LLM conferences as NeuRIPS, ICLR, ICML, EMNLP, ACL, NAACL with 500+ citations.

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

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

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.

Company

Amazon Development Centre (London) Limited

Job ID: A10599859