Description AWS Machine Learning accelerators are at the forefront of AWS innovation and one of several AWS tools used for building Generative AI on AWS. The Inferentia chip delivers best-in-class ML inference performance at the lowest cost in cloud. Trainium delivers the best-in-class ML training performance with the most teraflops (TFLOPS) of compute power for ML in the cloud. This is all enabled by cutting edge software stack, the AWS Neuron Software Development Kit (SDK), which includes an ML compiler, runtime and natively integrates into popular ML frameworks, such as PyTorch, TensorFlow and JAX. AWS Neuron is used at scale with customers like Snap, Autodesk, Amazon Alexa, Amazon Rekognition and more customers in various other segments. The Amazon Annapurna Labs team is responsible for silicon development at AWS. The team covers multiple disciplines including silicon engineering, hardware design and verification, software and operations. The Neuron Compiler team is developing a deep learning compiler stack that takes neural network descriptions created in frameworks such as TensorFlow, PyTorch, and JAX, and converts them into code suitable for execution. The team is comprised of some of the brightest minds in the engineering, research, and product communities, focused on the ambitious goal of creating a toolchain that will provide a quantum leap in performance. As a Machine Learning Compiler Engineer II in the AWS Neuron Compiler team, you will be supporting the ground-up development and scaling of a compiler to handle the world's largest ML workloads. Architecting and implementing business-critical features, publish cutting-edge research, and contributing to a brilliant team of experienced engineers excites and challenges you. You will leverage your technical communications skill as a hands-on partner to AWS ML services teams and you will be involved in pre-silicon design, bringing new products/features to market, and many other exciting projects. A background in compiler development is strongly preferred. A background in Machine Learning and AI accelerators is preferred, but not required. In order to be considered for this role, candidates must be currently located or willing to relocate to Cupertino (perferred), Seattle, Austin. Basic Qualifications - 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience - 2+ years of experience in developing compiler features and optimizations - Proficiency with 1 or more of the following programming languages: C++ (preferred), C, Python Preferred Qualifications - Master or PhD degree in computer science or equivalent - Proficiency with resource management, scheduling, code generation, and compute graph optimization - Experience optimizing Tensorflow, PyTorch or JAX deep learning models - Experience with multiple toolchains and Instruction Set Architectures Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $151,300/year in our lowest geographic market up to $261,500/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.