The Alexa Edge AI team under Alexa Ambient Computing Tech Organization is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to help build industry-leading technology with computer vision, speech and multimodal perception models for various edge applications.
Key job responsibilities
As an Applied Scientist with the Alexa Edge AI team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art with multimodal models with an emphasis on computer vision. Your work will directly impact our customers in the form of products and services that make use of CV technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances within perception domain.
A day in the life
An Applied Scientist with the Alexa Edge AI team will support science solution design, run experiments, research new algorithms, and find new ways of optimizing the customer experience; while setting examples for the team on good science practice and standards. Besides theoretical analysis and innovation, an Applied Scientist will also work closely with talented engineers and scientists to put algorithms and models into production.
About the team
The Alexa Edge AI team has a mission to deliver best in class, resource efficient multimodal AI models in support of various perception (vision, audio and speech) based applications for devices like Echo Show series within Amazon.- PhD, or Master's degree and 3+ years of CS, CE, ML or related field experience
- 1+ years of building models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms- Experience in Vision Language Models, Audio-Video fusion and multi-modal LLMs
- Experience with optimizing computer vision models for on-device deployment (pruning, quantization, distillation, etc.)
- Effective verbal and written communication skills with non-technical and technical audiences.
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