**Expert Vision Modeler II**
vor 3 Wochen
About the Role
At Amazon TA, we are revolutionizing the way we perceive and act in our world-class customer experience. We are looking for an Applied Scientist II who will build and deploy models that make smarter decisions on a wide array of multi-modal signals.
Key Responsibilities
You will build computer vision and multi-modal deep learning models that understand the state of products and packages flowing through our fulfillment network. You will work with very large real-world vision datasets, as well as a diverse set of multi-modal datasets, including natural language and structured data.
What You Will Do
You will adapt state-of-the-art machine learning and computer vision techniques to develop solutions for business problems in our fulfillment network. You will work with images, videos, natural language, and sequences of events from existing or new hardware. You will be part of an interdisciplinary team of scientists and engineers with deep expertise in developing cutting-edge AI solutions at scale.
About Amazon TA
Amazon TA 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.
Requirements
- 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
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