Applied Scientist Ii, Aft Ai, Amazon Fulfillment
vor 2 Wochen
PhD, or a Master's degree and experience in CS, CE, ML or related field
- Experience programming in Java, C++, Python or related language
- Experience with state-of-the-art deep learning and data science frameworks like PyTorch, TensorFlow, Pandas
- Experience working with deep learning models for classification tasks
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.
**Publicly available articles showcasing some of our work**:
Berlin, BE, DEU
- Experience in professional software development
- Experience in deploying deep learning models in production
- Experience training deep learning models with multi-modal input (vision-language models)
- Experience in self-supervised learning
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