Applied Scientist, AI Research & Education

Key Skills

Java Programming, Python Programming, AJAX, C++, C Programming, SQL, PyTorch, TensorFlow, MxNet, NeurIPS

Job Description

Job summary
AWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.

We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles).

Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.

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.

Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.

BASIC QUALIFICATIONS

• M.S. degree in Computer Science and Engineering, Electrical Engineering, Mathematics, Statistics, or related disciplines.
• 5+ years of experiences in machine learning, deep learning, distributed training, systems architecture, high performance computing, or related areas in earth science.
• Experience with deep learning frameworks such as Pytorch, TensorFlow, JAX, MXNet.</li>
• Proficiency in Python or C/C++.
• Excellent problem solving ability
• Strong verbal and written communication skills

PREFERRED QUALIFICATIONS

• Ph.D. degree in Computer Science and Engineering, Electrical Engineering, Mathematics, Statistics, or related disciplines.
• Published and/or presented papers at ICML, NeurIPS, ICLR, AISTATS, CVPR, ICCV, ECCV, OSDI, Eurosys, SC, UAI, AAAI, IJCAI, or similar top-tier conferences and events.
• Research experience in the area of deep learning for earth science, representation learning, transfer learning, deep learning, graph neural networks, computer vision, HPC, and large-scale distributed systems.