We’re looking for a Machine Learning Scientist in the Personalization team for our Edinburgh office experienced in generative AI and large models. You will be responsible for developing and disseminating customer-facing personalized recommendation models. This is a hands-on role with global impact working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization. You will lead the design of machine learning models that scale to very large quantities of data, and serve high-scale low-latency recommendations to all customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. You will work alongside a science team to delight customers by aiding in recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization. Successful candidates will have strong technical ability, focus on customers by applying a customer-first approach, excellent teamwork and communication skills, and a motivation to achieve results in a fast-paced environment. Our position offers exceptional opportunities forevery candidate to grow their technical and non-technical skills. If you are selected, you have the opportunity to make a difference to our business by designing and building state of the art machine learning systems on big data, leveraging Amazon’s vast computing resources (AWS), working on exciting and challenging projects, and delivering meaningful results to customers world-wide. Key job responsibilities Develop machine learning algorithms for high-scale recommendations problem Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative analysis and business judgement. Collaborate with software engineers to integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency. Report results in a manner which isboth statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment. We are open to hiring candidates to work out of one of the following locations: Edinburgh, MLN, GBR BASIC QUALIFICATIONS PhD in machine learning related field or equivalent years of experience and fundamental knowledge of machine learning Proven track record of leadership in generative AI, NLP and/or large models Experience programming in Java, C++, Python or related language Experience in building machine learning models for business application Experience in appliedresearch PREFERRED QUALIFICATIONS Experience with modeling tools such as PyTorch, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. Experience with large scale distributed systems such as Hadoop, Spark etc. Experience in building speech recognition, machine translation and naturallanguage processing systems (e.g., commercial speech products or government speech projects) 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. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice ((url removed)) to know more about how we collect, use and transfer the personal data of our candidates. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need an adjustment during the application and hiring process, including support for the interview or onboarding process, please contact the Applicant-Candidate Accommodation Team (ACAT), Monday through Friday from 7:00 am GMT - 4:00 pm GMT. If calling directly from the United Kingdom, please dial (phone number removed) ((url removed)(phone number removed)). If calling from Ireland, please dial (phone number removed) ((url removed)(phone number