As part of our Generative AI science team in Amazon AWS Bedrock, you will have the opportunity to impact millions of our customers by researching and building innovative algorithms that can optimize the inference engine of foundation models. You will gain hands-on experience with Amazon’s large-scale computing resources to accelerate advances in machine learning and foundation models. You will work alongside a supportive and collaborative team with a healthy mix of scientists and engineers to research and develop state-of-the-art technology for inference optimization.
About the team
AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (IoT), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services.
Why AWS
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empowers us to be proud of our differences.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture.
Mentorship and Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Diverse Experiences
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply.
Minimum Qualifications:
- PhD in CS, ML or related field, or Master with equivalent years of experience.
- Experience programming in Java, C++, Python or related language.
- Experience developing ML models and systems to deliver product capabilities, and demonstrated ability to own problems end-to-end.
- Patents or publications at top-tier peer-reviewed conferences or journals.
- Prior experience with one or more model inference optimization techniques (quantization, distillation) and fluent in developing inference optimization techniques in frameworks like PyTorch and CUDA.
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability.
- Prior experience in the training and fine-tuning of Large Language Models (LLMs), or experience with various inference engines.
- Deep understanding of GPU architectures and experience optimizing for different hardware platforms and systems preferred.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. For more information, please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.
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