Applied Scientist II (L5), AFT AI, Amazon AFT AI
DESCRIPTION
Are you excited about developing large language models and computer vision models that revolutionize Amazon's Fulfillment network? Are you looking for opportunities to apply state-of-the-art AI on real-world problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics, we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience — at Amazon scale. To this end, we are looking for an Applied Scientist who will build and deploy models that make smarter decisions on a wide array of multi-modal signals. Together, we will be pushing beyond the state of the art in optimizing one of the most complex systems in the world: Amazon's Fulfillment Network.
Key job responsibilities
In this role, you will build computer vision and multi-modal deep learning models that understand the state of products and packages flowing through Amazon’s fulfillment network. You will build models that solve challenging problems like product identification and damage detection on Amazon's entire retail catalog (billions of different items, thousands of new items every day). You will primarily work with very large real-world vision datasets, as well as a diverse set of multi-modal datasets, including natural language and structured data. You will face a high level of research ambiguity and problems that require creative, ambitious, and inventive solutions.
A day in the life
AFT AI delivers the AI solutions that empower Amazon’s fulfillment network to make smarter decisions. You will work on an interdisciplinary team of scientists and engineers with deep expertise in developing cutting-edge AI solutions at scale. You will work with images, videos, natural language, and sequences of events from existing or new hardware. You will adapt state-of-the-art machine learning and computer vision techniques to develop solutions for business problems in the Amazon Fulfillment Network.
About the team
Amazon Fulfillment Technologies (AFT) powers Amazon’s global fulfillment network. We invent and deliver software, hardware, and science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it.
AFT AI is spread across multiple locations in NA (Bellevue WA and Nashville, TN) and Europe (Berlin, Germany). We are hiring candidates to work out of the Berlin location.
Publicly available articles showcasing some of our work:
- Damage Detection: https://www.amazon.science/latest-news/the-surprisingly-subtle-challenge-of-automating-damage-detection
- Product ID: https://www.amazon.science/latest-news/how-amazon-robotics-is-working-on-new-ways-to-eliminate-the-need-for-barcodes
BASIC QUALIFICATIONS
- PhD, or a Master's degree and experience in CS, CE, ML or related field
- Experience in building models for business application
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience working with deep learning models for classification tasks
- Experience working with vision-language models and large language models
- Experience with state-of-the-art deep learning and data science frameworks like PyTorch, TensorFlow, Pandas
PREFERRED QUALIFICATIONS
- Experience in professional software development
- Experience in deploying deep learning models in production
- Experience developing deep learning models with multi-modal input (vision-language models)
- Experience in self-supervised learning
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
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