Are you excited about developing state-of-the-art Machine Learning, Natural Language Processing and Deep Learning algorithms and designs using large data sets to solve real world problems? Do you have proven analytical capabilities and can multi-task and thrive in a fast-paced environment? Do you want to build a foundation for your career after your Master's or Ph.D program at an industry-leading company?
You enjoy the prospect of solving real-world problems that, quite frankly, have not been solved at scale anywhere before. Along the way, you’ll get opportunities to be a fearless disruptor, prolific innovator, and a reputed problem solver—someone who truly enables machine learning to create significant impacts.
As an Applied Scientist, you will bring statistical modeling and machine learning advancements to data analytics for customer-facing solutions in complex industrial settings. You will be working in a fast-paced, cross-disciplinary team of researchers who are leaders in the field. You will take on challenging problems, distill real requirements, and then deliver solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products.
PhD degree or foreign equivalent in Computer Science or a related field or Master's degree with multiple years of experience in the job offered or a related occupation. One year of experience must involve: programming with Java, C++, or Python; and applied knowledge of natural language processing, or machine learning.
• Strong working knowledge of programming languages such as C/C++, Java, or Python (SciPy, RPy2, etc).
• Practical machine learning experience
Research experience related to machine learning, deep learning and NLP
• Published and/or presented papers at ICASSP, ICML, NIPS, KDD, CVPR or similar top-tier conferences and events.
Amazon is an Equal Opportunity Employer – Minority / Women / Disability / Veteran / Gender Identity / Sexual Orientation