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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied Scientist II, Alexa for Shopping - **Company:** Amazon.com, Inc - **Location:** London, United Kingdom - **Contract:** Permanent contract - **Skills:** Java (Programming Language), A/B Testing, Artificial Intelligence, Amazon Alexa, Data Analysis, Business Software, C++ (Programming Language), Profiling, Data Mining, Data Visualization, Software Debugging, Distributed Systems, Memory Management, Python (Programming Language), Machine Learning, Language Modeling, Natural Language Processing, Parsing, Data Driven Tests, Software Engineering, High Performance Computing, Large Language Models, Multi-Agent Systems, Prompt Engineering, Deep Learning, Model Validation, Information Technology - **Published:** November 12, 2026 - **Apply:** https://dejobs.org/x/x/41F83062FC404C91BCC11AB6FA473B8B/job/ ## About the Role * 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, * 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. ## 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. This innovative role focuses on developing and optimizing language model-powered (LLM/SLM) conversational experiences. The core emphasis is to get the best performance out LLMs/SLMs via careful and methodical instruction design, contextual grounding, informed choices of MCP tools and agent/multi-agent systems, context engineering, model fine-tuning, evaluation frameworks, and experimentation to systematically improve quality, robustness, and customer impact. The work combines scientific rigor with product intuition to systematically raise the bar for conversational AI performance at Amazon scale. Our mission in conversational shopping is to make it easy for customers to find and discover the best products to meet their needs by helping with their product research, providing comparisons and recommendations, answering product questions, enabling shopping directly from images or videos, providing visual inspiration, and more. We do this by leveraging advanced analytics, Natural Language Processing (NLP), Machine Learning (ML), A/B testing, causal inference, and data-driven insights to continuously improve our systems., 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.