Amazon's Product Assurance, Risk and Security (PARS) Team is looking for a smart, energetic, and creative Applied Scientist to apply and extend state-of-the-art research in NLP, probabilistic graphs, knowledge graphs, multi-modal inputs, domain adaptation, meta-learning, and large multi-label hierarchical classification to join the Machine Learning team in Seattle. At Amazon, we are working to be the most customer-centric company on earth. Millions of customers trust us to ensure a safe shopping experience. This is an exciting and challenging position to drive research that will shape new ML solutions for product and food safety, and restricted product compliance in order to achieve best-in-class, company-wide standards around product assurance.
You will analyze and process large amounts of tabular, textual, and product image data from historical orders, product detail pages, selling partner details and customer feedback, evaluate state-of-the-art algorithms and frameworks, and develop new algorithms to improve safety and compliance mechanisms. You will partner with engineers and product managers to design new ML solutions implemented across the entire Amazon product catalog.
· MS in Computer Science, Machine Learning, NLP or other relevant area
· 5+ years of experience in an industrial applied science setting, where your work was directly incorporated into production systems
· Proficiency in Python, and text data manipulation
· Understanding of computer science fundamentals such as data structures, object-oriented design and service-oriented architectures
· Ability to convey Machine Learning concepts and considerations to non-experts
· PhD in Computer Science, Machine Learning, NLP or other relevant area
· 7+ years of practical work experience in building, iterating and deploying production code in end-to-end multimodal solutions
· Experience working with distributed processing of terabytes of tabular and text data
· Significant peer reviewed scientific contributions in relevant field
· Experience defining organizational research and development practices in industry