- Palo Alto, CA
At Amazon AI, the Deep Engine Science team is working on machine learning systems to accelerate deep learning workloads on multiple hardware platforms, with the goal of making it easy for our customers to get good execution performance for machine learning models everywhere.
The AWS Deep Engine Science team is growing rapidly to keep up with the latest progress of the machine learning systems field to better serve our customers. We are hiring well-rounded applied scientists and software developers with backgrounds in machine learning, compilers, systems, and AI accelerators. If you have worked on HPC and performance tuning, you will enjoy working on the breadth of ML applications that we optimize.
As a machine learning systems developer/researcher, you will work on systematic approaches to improve the performance of deep learning models, with a focus on deep learning compilers such as Apache TVM. The work offers an extremely broad set of opportunities to work in full stack with exposure to multiple AI applications, ML frameworks, models, compilers, systems SW, and various AI hardware including ARM, Intel, Nvidia, Amazon AI accelerators, and emerging edge AI ASICs. Working at the frontier of the field, you will have the opportunity to publish in the top-tier systems and machine learning conferences.
Join the AWS Deep Engine Science team to develop machine learning systems to help AWS customers train and deploy machine learning models in the cloud and on edge devices at scale in production.
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.
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.
• Bachelor/Master's Degree in Computer Science or Engineering
• 4+ years of software development experience in high performance computing, machine learning, systems architecture, compiler, or related areas
• Be familiar with at least one of the following hardware platforms: x86 CPU, ARM CPU, Nvidia GPU, and deep learning accelerator
• Proven ability to develop and deliver an optimizing compiler for high level / domain specific programming language, such as publication and/or open-source projects
• 6+ years technical leadership role in machine learning, HPC, systems architecture, compiler, or related areas
• PhD in Computer Science
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
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