Applied Scientist, SageMaker Distributed Data Parallelism

Key Skills

Java Programming, Python Programming, C++, SQL, AWS, Machine learning techniques, Natural Language Processing (NLP), TensorFlow, Statistics, CUDA kernels, MxNet, AWS Sagemaker, C# Programming, Mathematics

Job Description

Are you ready to challenge the status quo for distributed deep learning model training? Are you excited to research and innovate techniques to co-design hardware- software and scientific techniques to train deep learning models with few million parameters to billions of parameters? Then, we think we can together push frontiers of deep learning model training capabilities! AWS AI group is looking to hire passionate Applied Scientists for SageMaker distributed data parallelism team (https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-intro.html) to join us in writing the future of deep learning model training software stack.

Some of the problems we are working on include:

• Optimizing large scale (100+ billion parameters, 1000s of GPU devices) distributed deep learning model training.
• Coming up with innovative solutions to communicate gradients during model training on AWS infrastructure.
• Bring these innovations into TensorFlow, PyTorch, deep learning frameworks enabling customers to train the models out of the box.

Every day will bring new and exciting challenges on the job while you:

• Learn and use advanced technologies - MPI, CUDA, BLAS libraries
• Collaborate with internal engineering and research teams, across leading technology companies around the world and the open source community - TensorFlow, PyTorch, Uber/Horovod, Intel/MKLDNN, NVIDIA/CUDA
• Collaborate with research teams to enable scientific advancements, with your innovations on systems for deep learning.

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.

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

• Master's Degree in computer science, statistics, engineering, mathematics, or related field;
• Specialization in machine learning, deep learning, natural language processing, computer vision, or related fields;
• Experience with machine learning frameworks and libraries (e.g. TensorFlow, PyTorch, MXNet);
• Programming experience with at least one modern language such as Java, C++, or C# including object-oriented design;

PREFERRED QUALIFICATIONS

• Ph.D. degree with specialization in machine learning, deep learning and related fields;
• Proficiency in the TensorFlow and/or PyTorch frameworks;
• Strong working knowledge of C++ programming language;
• Strong working knowledge of Python programming language;
• Experience with High Performance Computing systems;
• Experience with CUDA programming;
• Experience with Deep Learning model architectures for Natural Language Understanding or Computer Vision tasks;