Are you excited about developing state-of-the-art Machine Learning, Natural Language Processing, Deep Learning and Computer Vision 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 We offer flexible year round start dates for both intern and fulltime roles.
Notable teams include:
· Amazon AI (AWS)
· Alexa ML
· Alexa Brain
· Amazon Go
· Lab 126
· Ad Tech
Basic Qualifications • Currently enrolled or recently completed a graduate degree program (either MS or Ph.D.) in Electrical Engineering, Computer Science, Computer Engineering, Mathematics, or related field with specialization in machine learning, NLP, ASR, deep learning, computer vision or related fields. • Strong working knowledge of programming languages such as C/C++, Java, or Python (SciPy, RPy2, etc).
• Practical machine learning experience
Preferred Qualifications • Research experience related to machine learning, deep learning, NLP, computer vision • Published and/or presented papers at ICASSP, ICML, NIPS, KDD, CVPR or similar top-tier conferences and events.