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.
Key job responsibilities
- Collaborate with cross-functional engineers and scientists to advance the state of the art in multimodal model evaluations for devices, including audio, images, and videos
- Invent and validate reliability for novel automated evaluation methods for perception tasks, such as fine-tuned LLM-as-judge
- Develop and extend our evaluation frameworks to support expanding capabilities for multimodal language models
- Analyse large offline and online datasets using Python or similar to understand model gaps, develop methods to interpret model failures, and collaborate with training teams to enhance model capabilities for product use cases
- Work closely with other scientists, compiler engineers, data collection, and product teams to advance evaluation methods
A day in the life
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.
About the team
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.
BASIC QUALIFICATIONS
- Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- Experience with machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance
- Experience applying theoretical models in an applied environment
- Experience with data scripting languages (e.g. SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
PREFERRED QUALIFICATIONS
- Experience in a ML or data scientist role with a large technology company
- Experience in manipulating/transforming data, model selection, model training, cross-validation and deployment at scale
- Experience with Machine Learning and Large Language Model (LLM) fundamentals, including architecture, training/inference lifecycles, and training recipe refinement
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Data Scientist, Edge Ai Product Science in Cambourne employer: Evi Technologies Limited
At Amazon, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. As a Software Development Engineer, you'll have the opportunity to work with cutting-edge technologies in a fast-paced environment, while benefiting from continuous learning and growth opportunities alongside industry experts. With a focus on impactful projects that enhance customer experiences, our teams are dedicated to pushing technological boundaries and shaping the future of technology across multiple locations in the EMEA region.
