{"id":2604,"date":"2026-07-08T20:48:24","date_gmt":"2026-07-08T20:48:24","guid":{"rendered":"https:\/\/coderseditor.com\/itjobs\/job\/applied-scientist-ii-alexa-for-shopping-science-uk\/"},"modified":"2026-07-08T20:48:24","modified_gmt":"2026-07-08T20:48:24","slug":"applied-scientist-ii-alexa-for-shopping-science-uk","status":"publish","type":"job_listing","link":"https:\/\/coderseditor.com\/itjobs\/job\/applied-scientist-ii-alexa-for-shopping-science-uk\/","title":{"rendered":"Applied Scientist II, Alexa for Shopping Science UK"},"content":{"rendered":"

Applied Scientist II, Alexa for Shopping Science UK Overview<\/h2>\n\n\n\n\n\n\n\n\n
Company Name<\/strong><\/td>\nAmazon<\/td>\n<\/tr>\n
Job Role<\/strong><\/td>\nApplied Scientist II, Alexa for Shopping Science UK<\/td>\n<\/tr>\n
Qualifications<\/strong><\/td>\nNot Specified<\/td>\n<\/tr>\n
Category<\/strong><\/td>\nIT Jobs<\/td>\n<\/tr>\n
Job Type<\/strong><\/td>\nFull Time<\/td>\n<\/tr>\n
Location<\/strong><\/td>\nLondon<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

This role is for an applied scientist joining the Alexa for Shopping Science organization in London to help advance Amazon\u00e2\u0080\u0099s conversational shopping technology. The team is building AI-powered search and shopping assistance that supports customers throughout the full shopping journey, from discovery and comparison through to recommendations, product questions, and direct shopping help. The work centers on language-model-driven conversational experiences and aims to improve how well these systems perform in real customer scenarios.<\/p>\n

The position is strongly focused on making large and small language models work better in practice. That includes careful prompt and instruction design, grounding responses in relevant context, choosing and using tools for model agents, building agent and multi-agent systems, engineering context effectively, fine-tuning models, creating evaluation frameworks, and running experiments to improve quality, robustness, and customer impact. The role combines rigorous scientific thinking with product judgment to raise the standard of conversational AI at Amazon scale.<\/p>\n

What you will do<\/h3>\n
    \n
  • Develop and support language-model agents for shopping experiences, including automated evaluation pipelines, judge-based evaluation approaches, rubric creation, and dataset curation to measure subtle dimensions of response quality.<\/li>\n
  • Work with partner teams across the organization to test and refine techniques such as retrieval augmentation, context enrichment, prompt decomposition, and model fine-tuning or other post-training methods where they make sense.<\/li>\n
  • Take ownership of post-training smaller language models when latency and cost are critical, using methods such as supervised fine-tuning, preference optimization, and distillation to support fast conversational shopping experiences.<\/li>\n
  • Apply machine learning and deep learning methods to improve the final customer experience in areas such as ranking, relevance, personalization, and multimodal understanding.<\/li>\n
  • Design and evaluate agentic system architectures, making thoughtful decisions about single-agent versus multi-agent approaches, memory strategies, and tool orchestration so the system performs well in terms of quality, speed, and reliability.<\/li>\n
  • Use very large-scale data to identify opportunities for model improvements and to make conversational systems more effective and performant.<\/li>\n
  • Analyze large multimodal interaction datasets to understand customer behavior and identify ways to improve response quality and the overall shopping experience.<\/li>\n
  • Use statistical methods, experimentation, and data analysis to build scalable measurement and optimization approaches for LLM-based shopping assistants, using both structured and unstructured context signals.<\/li>\n
  • Investigate issues and opportunities that affect conversational relevance, grounding, customer satisfaction, and downstream business outcomes.<\/li>\n
  • Work closely with product managers and engineers to turn analysis into production systems, including model evaluation and deployment activities.<\/li>\n
  • Explain findings clearly to both technical and non-technical audiences through presentations, written reports, and data visualizations.<\/li>\n<\/ul>\n

    Team and environment<\/h3>\n

    The Alexa for Shopping Science team is based in London and works alongside roughly 150 engineers, designers, and product managers. The group is shaping the future of AI-driven shopping at Amazon and contributes to many parts of the conversational AI stack.<\/p>\n

    The team\u00e2\u0080\u0099s scope includes making the assistant more agentic, such as enabling price alerts or allowing the assistant to act on a customer\u00e2\u0080\u0099s behalf and purchase items automatically when conditions are right. It also includes understanding multimodal queries and generating responses that combine text, images, audio, and video. In addition, the team works on deep research-style outputs that search both the web and Amazon\u00e2\u0080\u0099s catalog to provide detailed, personalized shopping guidance.<\/p>\n

    The science group uses and advances modern techniques in natural language processing, generative AI, information retrieval, machine learning, deep learning, and data mining. The team also validates its work by taking part in internal and external scientific communities.<\/p>\n

    What Amazon is looking for<\/h3>\n
      \n
    • A PhD, or a master\u00e2\u0080\u0099s degree plus relevant experience, in computer science, computer engineering, machine learning, or a related field.<\/li>\n
    • Experience designing advanced deep learning model architectures, training and optimizing deep learning systems, and applying model pruning.<\/li>\n
    • A history of patents or publications in leading peer-reviewed conferences or journals.<\/li>\n
    • Programming ability in Java, C++, Python, or a similar language.<\/li>\n
    • Experience in one or more of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel or distributed computing, or high-performance computing.<\/li>\n
    • Experience building machine learning models for business applications.<\/li>\n<\/ul>\n

      Preferred background<\/h3>\n
        \n
      • Deep practical expertise in post-training large language models, including supervised fine-tuning and large-scale reinforcement learning, especially in distributed training settings.<\/li>\n
      • A strong publication record in leading NLP and LLM conferences such as NeurIPS, ICLR, ICML, EMNLP, ACL, or NAACL, with a citation count above 500 noted as a plus.<\/li>\n<\/ul>\n

        Additional information<\/h3>\n

        Amazon says it is an equal opportunities employer and bases hiring decisions on experience and skills. The company also states that it values a diverse workforce and believes inclusion is central to its success.<\/p>\n

        Amazon notes that it protects candidate privacy and security, and it provides a privacy notice describing how applicant data is collected, used, and transferred.<\/p>\n

        If you need a workplace accommodation or adjustment during the application, interview, or onboarding process because of a disability, Amazon says support is available. If your country or region is not listed in the accommodation guidance, you are directed to contact the recruiting partner.<\/p>\n

        The posting is for a role in London, UK, and the page does not describe a remote or hybrid working arrangement.<\/p>\n\n

        Degree Requirement:<\/strong> Not Specified<\/p>\n

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