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Amazon

Applied Scientist III, Alexa & Fire TV Security

Amazon, Boston, Massachusetts, us, 02298


Job ID: 2611763 | Amazon.com Services LLCThe Devices and Services Security team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to secure the development of industry-leading Generative AI systems.

Please double check you have the right level of experience and qualifications by reading the full overview of this opportunity below.

As a Senior Applied Scientist with the Devices & Services Security team, you will lead the development of novel algorithms and modeling techniques to advance the state of the art with Generative AI systems. Your work will directly impact our customers in the form of products and services that make use of vision and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate development of security solutions with multimodal Large Language Models (LLMs) and Generative Artificial Intelligence (Gen AI).

The Devices and Services (D&S) Security team works to ensure that Amazon devices and services are designed and implemented to the high standards required to maintain and enhance customer trust. The team develops security technologies for builder teams, performs penetration testing, and handles and tracks incident responses to resolution. The team is responsible for defining and executing on the security and privacy requirements for the entire organization.

We are open to hiring candidates to work out of one of the following locations:

Bellevue, WA, USA | Boston, MA, USA | Phoenix, AZ, USA | Seattle, WA, USA | Sunnyvale, CA, USABASIC QUALIFICATIONS - PhD, or Master's degree and 6+ years of applied research experience- 3+ years of building machine learning models for business application experience- Experience programming in Java, C++, Python or related language- Experience with neural deep learning methods and machine learningPREFERRED QUALIFICATIONS - Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability- Experience using managed ML/AI solutions- Experience with learning LLMs and Gen AI in Computer Vision, both in the image and video domains.

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