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Applied Scientist - AWS

Job ID: 1730633 | Amazon Dev Center U.S., Inc.

DESCRIPTION

AWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on foundation models, large-scale representation learning, and distributed learning methods and systems. At AWS AI/ML you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and innovate on new representation learning solutions. 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.

Large-scale foundation models have been the powerhouse in many of the recent advancements in computer vision, natural language processing, automatic speech recognition, recommendation systems, and time series modeling. Developing such models requires not only skillful modeling in individual modalities, but also understanding of how to synergistically combine them, and how to scale the modeling methods to learn with huge models and on large datasets. Join us to work as an integral part of a team that has diverse experiences in this space. We actively work on these areas:
· Multi-modal learning
· Multi-task learning
· Model compression, distillation, sparsification, quantization
· Accelerated optimization methods
· Privacy and federated learning
· Transfer learning
· Model robustness and faithful generation
· Explainability
· Large-scale distributed systems

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

· 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.
· Experience with deep learning frameworks such as Pytorch, TensorFlow, JAX, MXNet.
· 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: representation learning, natural language processing, transfer learning, deep learning, graph neural networks, knowledge distillation, explainability, model robustness and faithful generation, recommender systems, search, accelerated optimization methods, HPC, and large-scale distributed systems.

Amazon is committed to a diverse and inclusive workforce. Amazon is an equal opportunity employer and does not discriminate on the basis of race, ethnicity, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us