Computer Vision - Applied Scientist, AWS AI Labs: Computer Vision

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Join us in a historic endeavour to make Computer Vision accessible to the world with breakthrough research! The AWS AI Labs: Vision team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists develops the algorithms and models that have powered AWS computer vision services such as Amazon Rekognition, Amazon Textract, and Amazon Lookout for Vision. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops.

AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world.

Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods active learning and semi-automated data annotation, large scale image and video recognition, face detection and recognition, document OCR and scene text recognition, document understanding, 3D scene and layout understanding, and localization and mapping.

We are hiring for all our locations in the USA (Seattle, Pasadena, Bay Area) and Israel (Haifa and Tel Aviv).

About Us
Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Work/Life Balance
Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.

BASIC QUALIFICATIONS

• Graduate degree (MS or PhD) in Computer Science, Electrical Engineering, Mathematics or Physics
• Minimum 3+ years of research experience or 2+ years of work experience developing and commercializing computer vision or deep learning
• 2+ years of experience implementing computer vision or deep learning algorithms in C++, C, Python or equivalent programming languages

PREFERRED QUALIFICATIONS

• Experience with conducting research in a corporate setting
• Excellent publication record in peer reviewed conferences and journals
• Proven expertise in conducting independent research and building computer vision systems.