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 a global impact, working with a team of world-class engineers and scientists across the Edinburgh offices and wider organization.
You will work on machine learning models that scale to very large quantities of data and serve high-scale, low-latency recommendations to customers worldwide. You will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of your methods. Collaborating with a science team, you will aim to improve recommendation relevancy and enhance Amazon's reputation as a global leader in machine learning and personalization.
Successful candidates will have strong technical skills, a customer-first approach, excellent teamwork and communication skills, and motivation to achieve results in a fast-paced environment. Our position offers opportunities to grow both technical and non-technical skills. Selected candidates will have the chance to make a difference by designing and building state-of-the-art machine learning systems on big data, leveraging Amazon’s AWS resources, working on challenging projects, and delivering meaningful results worldwide.
Key job responsibilities
1. Develop machine learning algorithms for high-scale recommendation problems.
2. Design, prototype, and test hypotheses in a high-ambiguity environment, using quantitative analysis and business judgment.
3. Collaborate with software engineers to integrate successful experiments into large-scale Amazon production systems capable of handling hundreds of thousands of transactions per second with low latency.
4. Report results in a statistically rigorous and relevant manner, exemplifying good scientific practice in a business environment.
Minimum qualifications
* Master's degree in computer science, mathematics, statistics, machine learning, or a related quantitative field.
* Experience implementing algorithms using toolkit libraries and self-developed code.
* Publications at top-tier peer-reviewed conferences or journals.
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We are committed to an inclusive culture that empowers Amazonians to deliver the best results. If you need workplace accommodations during the application process, visit this link for more information. If your country/region isn't listed, please contact your Recruiting Partner.
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