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Now, we’re on the cusp of a new era, where AI holds the promise to reshape society and business in unprecedented ways. As part of our world-class team of AI experts—scientists, engineers, researchers, product builders, and public policy experts—here, you’ll have the opportunity to unlock the next frontiers of artificial intelligence and machine learning.Key job responsibilities
• Collaborate with experienced cross-disciplinary Amazonians to conceive, design, and bring innovative products and services to market.
• Design and build innovative technologies in a large distributed computing environment, and help lead fundamental changes in the industry.
• Create solutions to run predictions on distributed systems with exposure to innovative technologies at incredible scale and speed.
• Build distributed storage, index, and query systems that are scalable, fault-tolerant, low cost, and easy to manage/use.
• Ability to design and code the right solutions starting with broadly defined problems.
• Work in an agile environment to deliver high-quality software.
- Currently enrolled in a Bachelors, Masters, or PhD in Computer Science, Computer Engineering, Data Science, Electrical Engineering, or majors relating to these fields.
- To quality, applicants should have an expected graduate date between May 2023 - Fall 2025 and/or graduated within the last 24 months
- Knowledge of computer science fundamentals such as object-oriented design, operating systems, algorithms, data structures, and complexity analysis
- Are 18 years of age or older.
- Experience with at least one modern language such as Java, Python, C++, or C# including object-oriented design
- Candidates with strong interests and academic qualifications/research focus in: Artificial Intelligence, machine learning, and/or Generative AI, such as computer vision, deep learning models, XLA, TVM, MLIR, and LLVM.
- Experience in optimization mathematics such as linear programming and nonlinear optimization.
- Experience with distributed, multi-tiered systems, algorithms, and relational databases.
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