Applied Scientist II, AI Research & Education Full-time

AWS AI Research & Engineering (AIRE) is looking for scientists and engineers to work on optimizing foundation models for inference in Tuebingen, Germany. At AIRE, we actively work on applying compiler, high-performance computing, and computer architecture techniques, amongst others, to optimize the performance of foundation model execution, including training and inference. Join us to work as an integral part of a team that has diverse experiences in this space. You will invent, implement, and deploy state of the art machine learning algorithms and systems to improve the inference of foundation models. To this end, you will interact closely with our customersproduct orgsand with the academic and research communities.

About AWS

Diverse Experiences

AWS 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. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

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Inclusive Team Culture

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Work/Life Balance

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About the team

Utility Computing (UC)

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.

BASIC QUALIFICATIONS

– PhD, or a Master’s degree and experience in CS, CE, ML or related field

– Experience in patents or publications at top-tier peer-reviewed conferences or journals

– Experience programming in Java, C++, Python or related language

– Experience with Machine Learning

– Experience with mathematical optimization, parallel and distributed computing, high-performance computing

– Solid technical understanding of modern deep learning architectures like Transformers

– Experience with with deep learning frameworks like PyTorch

PREFERRED QUALIFICATIONS

– Experience with programming hardware accelerators (e.g., GPU / TPU / Neuron)

– Experience with inference optimization of foundation models (e.g., model compression techniques like distillation/pruning/sparsification/quantization, architectural optimization like mixture of experts, decoding optimization like speculative decoding/adaptive inference, system-level optimization like distributed inference/persisting KV caching/dynamic batching)

– Experience with inference engines (e.g., vLLM, TGI)

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