Sr. Software Engineer- AI/ML, AWS Neuron

Shape the Future of AI Accelerators at AWS Neuron We build Amazon Neuron, the software development kit used to accelerate deep learning and GenAI workloads on Amazon’s custom machine learning accelerators, Inferentia and Trainium. As a Senior Software Engineer on our Machine Learning Applications team, you will optimize the world's most demanding AI models at a scale few engineers ever get to work on. This role offers a unique opportunity to work at the intersection of machine learning, high-performance computing, and distributed architectures, where you'll help shape the future of AI acceleration technology. You can learn more about Neuron https://awsdocs-neuron.readthedocs-hosted.com https://aws.amazon.com/machine-learning/neuron/ https://github.com/aws/aws-neuron-sdk https://www.amazon.science/how-silicon-innovation-became-the-secret-sauce-behind-awss-success Key job responsibilities Key job responsibilities This role will help lead the efforts in building distributed inference support for Pytorch in the Neuron SDK. This role will tune these models to ensure highest performance and maximize the efficiency of them running on the customer AWS Trainium and Inferentia silicon and servers. Strong software development using Python, System level programming and ML knowledge are both critical to this role. Our engineers collaborate across compiler, runtime, framework, and ha [... source excerpt omitted ...] e intersection of software, hardware, and machine learning systems, you'll bring expertise in low-level optimization, system architecture, and ML model acceleration. In this role, you will: * Design, develop, and optimize machine learning models including GPT, Kimi, and Qwen on custom AI accelerators. * Participate in all stages of the ML system development lifecycle including distributed computing based architecture design, implementation, performance profiling, low level optimizations, and production deployment. * Build infrastructure to systematically analyze and onboard multiple models with diverse architecture. * Design and implement high-performance kernels and features [... source excerpt omitted ...] ed performance analysis using profiling tools to identify and resolve bottlenecks * Implement optimizations such as fusion, sharding, tiling, and scheduling * Work directly with customers to enable and optimize their ML models on AWS accelerators * Collaborate across teams to develop innovative optimization techniques • Develop kernels to improve model efficiency on Amazon AI Accelerators • Transform complex tensor operations into highly optimized graph implementations • Optimize state-of-the-art language, vision, and multimodal generative AI models for Neuron hardware What Makes This Role Unique: • Direct influence on AWS's AI Accelerator used by thousands of ML applicat