Software Dev Engineer II, Rufus Stores Foundational AI -SFAI

Requirements

• 3+ years of non-internship professional software development experience
• 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
• Experience programming with at least one software programming language
• Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
• Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT
• Knowledge of system performance, memory management, and parallel computing principles
• Experience building data pipelines or automated ETL processes
• Experience using data and metrics to drive actionable insights at scale
• Experience with CUDA/C++/Kernel development
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• USA, CA, Palo Alto - 165,200.00 - 223,600.00 USD annually
• USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually

Additional Information

We're working to improve shopping on Amazon using the capabilities of large language models (LLM), and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists and engineers to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey! Key job responsibilities Key job responsibilities In this role you will leverage both your engineering and machine learning background to help develop generative AI for shopping. On a day-to-day basis, you will: - Design and implementation of a stable and efficient training system for model training and reinforcement learning that scale to various of model sizes and architecture. - Collaborate with other talented applied scientists and engineers to improve training efficiency and reliability that accelerates innovation. - Design and implement scalable data infrastructure: that handle Amazon-scale data ingestion, processing, and delivery across different training and evaluation stages; - Quickly learn and adopt state-of-the-art technologies and algorithms in the field of Generative AI.

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