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Applied Scientist, Generative AI Innovator
1 month ago
We are seeking an Applied Scientist to join our Generative Artificial Intelligence (AI) Innovation Center team at Amazon Web Services (AWS). As an Applied Scientist, you will partner with technology and business teams to build innovative solutions that surprise and delight our customers.
Key Responsibilities- Collaborate with scientists and engineers to research, design, and develop cutting-edge generative AI algorithms to address real-world challenges.
- Work across customer engagement to understand what adoption patterns for generative AI are working and rapidly share them across teams and leadership.
- Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths for generative AI.
- Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder.
- Provide customer and market feedback to Product and Engineering teams to help define product direction.
- 2+ years of building machine learning models or developing algorithms for business application experience.
- Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field.
- Knowledge of programming languages such as C/C++, Python, Java or Perl.
- Proven knowledge of deep learning and experience hosting and deploying ML solutions (e.g., for training, tuning, and inferences).
- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field.
- Working knowledge of generative AI and hands-on experience in prompt engineering, deploying and hosting Large Foundational Models.
- Hands-on experience building models with deep learning frameworks like Tensorflow, PyTorch, or MXNet.
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