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Machine Learning Engineer
2 months ago
We are seeking a highly skilled Machine Learning Scientist to join our Personalization team in Edinburgh. The ideal candidate will have expertise in generative AI and large models, with a proven track record of leadership in machine learning-related fields.
The successful candidate will lead the design of machine learning models that scale to large quantities of data, serving high-scale low-latency recommendations to customers worldwide. They will embody scientific rigor, designing and executing experiments to demonstrate the technical efficacy and business value of their methods.
The Machine Learning Scientist will work alongside a science team to delight customers by improving recommendations relevancy, and raise the profile of Amazon as a global leader in machine learning and personalization.
Key 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 is both statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment
- 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 applied research
- Experience with modeling tools such as PyTorch, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
