Amazon Sr Applied Scientist AI Solutions 

United States, Washington, Seattle 

59915823

We'e looking for experienced applied scientist to drive the development and implementation of state-of-the-art algorithms and models for supervised fine-tuning and reinforcement learning through human feedback and and complex reasoning.You will play a critical role in supporting the development of GenAI products that can handle AWS-scale use cases and have a significant impact on our customers' experiences.Key job responsibilities- Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results
- Think big about the arc of development of Gen AI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems
- Communicate results and insights to both technical and non-technical audiences
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning
- Experience using managed ML/AI solutions
- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.