<strong>Description<br><br></strong>Amazon Science gives you insight into the company’s approach to customer focused scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work.<br><br>Please visit https://www.amazon.science for more information.<br><br>Are you an expert in Natural Language Processing (NLP) and Large Language Models (LLM)? Are you interested in building Generative AI solutions on complex business problems that have significant global benefit. The Brand Protection team designs and builds high performance AI systems using machine learning that identify and prevent abuse and counterfeit on behalf of brand owners worldwide.<br><br>We are looking for a highly talented scientist to help build of our vision for Brand Protection. As a senior applied scientist on the team, you will use NLP and LLM techniques to understand and extract key information from product detail page, built automated AI solutions that thinks like human to assist decision making and eventually make autonomous decisions. You will work backwards from data insights and customer feedback to build the right machine learning solutions, and resourceful in finding innovative solutions to unsolved problems. You will work closely will product team and engineering partners to launch the solution into production and owning the end-to-end solution.<br><br>An ideal candidate should have extensive experience driving Machine Learning initiatives, specially in NLP and LLM applications, from conception to launch in a rapidly evolving environment. Amazon’s growth requires leaders who move fast, have an entrepreneurial spirit to create new solutions, have an unrelenting tenacity to get things done, and are capable of breaking down and solving complex problems.<br><br><strong>Major Responsibilities<br><br></strong><ul><li> Understand business challenges by analyzing data and customer feedback</li><li> Collaborate with tech and product teams on building ML strategies, experimentation, implementation and continuous improvement</li><li> Analyze and extract relevant information from large amounts of both structured and unstructured data to design strategies to solve business problems.</li><li> Use NLP, LLM and machine learning techniques to create scalable solutions for business problems</li><li> Create business and analytics reports and present to the senior management teams</li><li> Research and implement novel AI solutions and publish research papers<br><br></li></ul><strong>About The Team<br><br></strong>Here at Selling Partner Services, we embrace our differences. We are committed to furthering our culture of inclusion. We have 14 employee-led affinity groups, reaching 10,000+ employees in chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our DEI Ambassador Program. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.<br><br><strong>Basic Qualifications<br><br></strong><ul><li> PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 4+ years of building machine learning models or developing algorithms for business application experience</li><li> 3+ years of building machine learning models for business application experience</li><li> Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)</li><li> Experience with neural deep learning methods and machine learning</li><li> Experience programming in Java, C++, Python or related language</li><li> PhD, or Master's degree and 6+ years of applied research experience</li><li> 5+ years of applied research experience<br><br></li></ul><strong>Preferred Qualifications<br><br></strong><ul><li> Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.</li><li> Experience with large scale distributed systems such as Hadoop, Spark etc.<br><br></li></ul>Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.<br><br>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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.<br><br>Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.<br><br><br><strong>Company</strong> - Amazon.com Services LLC<br><br>Job ID: A2943034