Applied Scientist, Rbs Tech

Description RBS Retail Business Services Tech team works towards enhancing the customer experience CX and their trust in product data by providing technologies to find and fix Amazon CX defects at scale Our platforms help in improving the CX in all phases of customer journey including selection discoverability fulfilment buying experience and post-buying experience product quality and customer returns The team also develops GenAI platforms for automation of Amazon Stores Operations As a Sciences team in RBS Tech we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience CX and Selling partner experience SPX We work to solve problems related to multi-modal understanding text and images task automation through multi-modal LLM Agents supervised and unsupervised techniques multi-task learning multi-label classification aspect and topic extraction for Customer Anecdote Mining image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results Key job responsibilities As an Applied Scientist you will be responsible to design and deploy scalable GenAI NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues You will develop novel LLM deep learning and statistical techniques for task automation text processing image processing pattern recognition and anomaly detection problems You will define the research and experiments strategy with an iterative execution approach to develop AI ML models and progressively improve the results over time You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation You will independently file for patents and or publish research work where opportunities arise The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems Therefore the Applied Scientist role will impact the large product strategy identifies new business opportunities and provides strategic direction which is very exciting Basic Qualifications - Master s degree - Experience with video and image processing and compression algorithms and standards computer vision and or machine learning - Knowledge of programming languages such as C C Python Java or Perl - Experience in solving business problems through machine learning data mining and statistical algorithms - Experience in patents or publications at top-tier peer-reviewed conferences or journals - Knowledge of architectural concepts and algorithms schedule tradeoffs and new opportunities with technical team members - Experience in problem solving and delivering results - 2 years of relevant applied science research experience with a Masters degree in Electrical Engineering Computer Science Computer Engineering Mathematics or related field with specialization in machine learning NLP Computer Vision deep or related fields - Excellent communication skills Preferred Qualifications - Have publications at top-tier peer-reviewed conferences or journals - Strong verbal written communication skills including an ability to effectively collaborate with both research and technical teams - Scientific thinking and the ability to invent a track record of thought leadership and contributions that have advanced the field - Solid understanding of machine learning deep learning algorithms and computational complexity - Strong CS fundamentals in data structures problem solving algorithm design and complexity analysis Our inclusive culture empowers Amazonians to deliver the best results for our customers If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information If the country region you re applying in isn t listed please contact your Recruiting Partner