Amazon Sr Applied Scientist AWS Generative AI Innovation Center 

United States, Texas, Arlington 

872451657

External job description
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations.The Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.Key job responsibilities
The primary responsibilities of this role are to:
- Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industriesDiverse Experiences
Amazon 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.Why AWS
Work/Life BalanceMentorship and 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.

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience with neural deep learning methods and machine learning

- PhD degree in computer science, engineering, mathematics, operations research, or in a highly quantitative field
- Practical experience in solving complex problems in an applied environment
- Hands on experience building models with deep learning frameworks like MXNet, Tensorflow, or PyTorch
- Strong communication skills, with attention to detail and ability to convey rigorous mathematical concepts and considerations to non-experts
- Comfortable working in a fast paced, highly collaborative, dynamic work environment
- Scientific thinking and the ability to invent, a track record of thought leadership and contributions that have advanced the field.