Amazons International Technology org in EU (EU INTech) is creating new ways for Amazon customers discovering Amazon catalog through new and innovative Customer experiences. Our vision is to provide the most relevant content and CX for their shopping mission. We are responsible for building the software and machine learning models to surface high quality and relevant content to the Amazon customers worldwide across the site.
The team mainly located in Madrid Technical Hub London and Luxembourg comprises Software Developer and ML Engineers Applied Scientists Product Managers Technical Product Managers and UX Designers who are experts on several areas of matching ranking LLMs computer vision recommendations systems Search as well as CX. Are you interested on how the experiences that fuel Catalog and Search are built to scale to customers WW Are interesting on how we use state of the art AI to generate and provide the most relevant content
Key job responsibilities
We are looking for Applied Scientists who are passionate to solve highly ambiguous and challenging problems at global scale. You will be responsible for major science challenges for our team including working with LLMs text to image and image to text state of the art models to scale to enable new Customer Experiences WW. You will design develop deliver and support a variety of models in collaboration with a variety of roles and partner teams around the world. You will influence scientific direction and best practices and maintain quality on team deliverables.
PhD
Knowledge of programming languages such as C/C Python Java or Perl
Experience building machine learning models or developing algorithms for business application
Experience developing and implementing deep learning algorithms particularly with respect to computer vision algorithms
PhD in engineering technology computer science machine learning robotics operations research statistics mathematics or equivalent quantitative field
Experience in stateoftheart deep learning models architecture design and deep learning training and optimization and model pruning
Experience with popular deep learning frameworks such as MxNet and Tensor Flow
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