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Amazon Devices is seeking a Data Scientist to join the Edge AI Product Science team and advance multimodal deep learning models across product lines. You will curate data, build annotation pipelines, and contribute to model evaluation methods for devices including audio and vision experiences.
The role emphasizes a data-first approach, collaboration across teams of engineers and scientists, and production-ready evaluation frameworks for edge devices.
Amazon Devices is an inventive research and development company that designs and engineers high-profile devices like Echo, Fire Tablets, Fire TV, and other consumer devices. We are looking for an exceptional data scientist to join our Edge AI Product Science team to advance the state-of-the-art in developing efficient multimodal deep learning models across our product portfolio. Through close hardware-software integration, we design and train models for resource efficiency across the hardware and software tech stack.
Edge AI Product Science team is looking for a talented Data Scientist who will introduce and model best practices around our dataset curation, annotation pipelines, and data driven model development. This role will take a data-first approach to improving model and system performance and work alongside other scientists to develop new evaluation methods for multimodal language models and agents for our devices, including audio and vision experiences.
As a Data Scientist with the Edge AI Product Science team, you'll contribute to innovative methods for evaluating new product experiences and discover ways to use data to enhance our model capabilities and enrich our customer experiences. You'll have opportunities to collaborate across teams of engineers and scientists to bring algorithms and models from inception through prototype all the way to production.
Our Edge AI Product Science team brings together our unique skills and experiences to deliver state-of-the‑art multimodal AI models that enable new experiences on Amazon devices. We strive to deliver customer experiences driven by AI at the edge which are only possible with tight model, hardware, and system co‑design.