Applied Scientist, GraphML

Key Skills

Java Programming, Python Programming, C++, C Programming, Framework, SQL, AWS, Machine learning techniques, Deep Learning, SPARK Programming, Neural Networks, NeurIPS

Job Description

Are you interested in bringing bleeding edge machine learning methods into broader industrial use? We are looking for talented Applied Scientists that are motivated to tackle tough problems and help bring Graph Machine Learning (GraphML) methods to our customers through our database products at Amazon Web Servivces (AWS).

As an Applied Scientist on the Neptune ML Team (https://aws.amazon.com/neptune/machine-learning/) you will be working with a team of scientists and engineers to implement fast, efficient and highly scalable machine learning algorithms that are applied to graph structured data. Neptune ML is a service that makes it easy to build Graph Neural Network (GNN) models on graph data stored in Neptune, our managed graph database service. Neptune ML enables customers with limited ML experience to build sophisticated GNNs.

You'll work with Applied Scientists and engineers within Amazon AI and Amazon Neptune and develop new features. If you are a talented, detail-oriented and enthusiastic researcher who is passionate about new technologies including big data and machine learning, then this is the right team for you!

About the team

Inclusive Team Culture

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. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.

BASIC QUALIFICATIONS

• M.S. degree in Computer Science and Engineering, Electrical Engineering, Mathematics, or related disciplines.
• 4+ years of experience in areas such as data technologies, analytics, machine learning, natural language processing, deep learning, or related areas.
• Experience with deep learning frameworks such as Pytorch.
• Experience working with big data tools like Spark
• Proficiency in Python or C/C++.

PREFERRED QUALIFICATIONS

• Ph.D. degree in Computer Science and Engineering, Electrical Engineering, Mathematics, Statistics, or related disciplines.
• Published and/or presented papers at ACL, NAACL, EMNLP, AACL, ICML, NeurIPS, AISTATS, CVPR, ICCV, ECCV, OSDI, Eurosys, SC, SIGCOMM or similar top-tier conferences and events.
• Research experiences in the following areas: graph neural networks, knowledge graphs, deep learning.