Sr. Applied Scientist, Generative Ai Innovation Center
vor 1 Monat
Sr.
Applied Scientist, Generative AI Innovation Center Machine learning (ML) has been strategic to Amazon from the early years.
We are pioneers in areas such as recommendation engines, product search, e Commerce 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 problems and promote innovation in their organization.
As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse problems 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.
We're looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others.
Key Job Responsibilities The primary responsibilities of this role are to: Design, develop, and evaluate innovative ML models to solve diverse problems and opportunities across industries.
Interact with customers directly to understand their business problems and help them with defining and implementing scalable Generative AI solutions to solve them.
Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solutions.
Minimum Requirements - 5+ years of building machine learning models for business application experience.- Ph D, or Master's degree and 5+ years of applied research experience.- Experience programming in Java, C++, Python or related language.- Experience with neural deep learning methods and machine learning.- Experience in patents or publications at top-tier peer-reviewed conferences or journals.- Experience in professional software development.- Ph D 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 Py Torch.- 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.
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