Amazon
The Product:
AWS Machine Learning accelerators are leading the charge in innovation at AWS. Our Inferentia chip offers top-tier ML inference performance at the most competitive cost in the cloud. Trainium promises unmatched ML training performance, delivering extensive teraflops (TFLOPS) for cloud ML tasks. This is made possible through our cutting-edge AWS Neuron Software Development Kit (SDK), which comprises an ML compiler, runtime, and seamless integration into popular ML frameworks like PyTorch, TensorFlow, and MxNet. Customers like Snap, Autodesk, Amazon Alexa, and Amazon Rekognition utilize AWS Neuron and Inferentia at scale across various sectors. The Team:
The Amazon Annapurna Labs team is crucial for silicon development at AWS, encompassing multiple disciplines including silicon engineering, hardware design, verification, software, and operations. The AWS Neuron team focuses on optimizing the performance of complex neural net models on our custom-built AWS hardware. Our dedicated group is developing a deep learning compiler stack that transforms neural network descriptions from frameworks such as TensorFlow, PyTorch, and MXNet into executable code. Together, we aim for a significant leap in performance for ML workloads. You:
In the role of Senior Machine Learning Compiler Engineer on the AWS Neuron team, you will spearhead essential features and initiatives for an advanced compiler designed for extensive ML workloads. Your responsibilities will include architectural design, publishing pioneering research, and mentoring passionate engineers. You will collaborate closely with AWS ML services teams and be involved in pre-silicon design, bringing innovative products and features to market, along with many other exciting projects. A background in Machine Learning and AI accelerators is advantageous but not mandatory. Basic Qualifications: 5+ years of professional software development experience. 5+ years of programming experience in at least one software language. 5+ years of experience in leading design or architecture of systems, focusing on reliability and scalability. 5+ years of involvement in the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations. Experience mentoring or leading tech teams. Preferred Qualifications: Bachelor's degree in computer science or equivalent. We are committed to fostering a diverse and inclusive work environment. Amazon is an equal opportunity employer, and we do not discriminate based on race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or any other legally protected status. Located in Cupertino, Seattle, Austin, or Toronto, this role has a salary range of $151,300 to $261,500, based on factors including location and experience. Our total compensation package may include equity, sign-on bonuses, and comprehensive benefits. This position will remain active until filled, and applicants should apply via our internal or external career site.
AWS Machine Learning accelerators are leading the charge in innovation at AWS. Our Inferentia chip offers top-tier ML inference performance at the most competitive cost in the cloud. Trainium promises unmatched ML training performance, delivering extensive teraflops (TFLOPS) for cloud ML tasks. This is made possible through our cutting-edge AWS Neuron Software Development Kit (SDK), which comprises an ML compiler, runtime, and seamless integration into popular ML frameworks like PyTorch, TensorFlow, and MxNet. Customers like Snap, Autodesk, Amazon Alexa, and Amazon Rekognition utilize AWS Neuron and Inferentia at scale across various sectors. The Team:
The Amazon Annapurna Labs team is crucial for silicon development at AWS, encompassing multiple disciplines including silicon engineering, hardware design, verification, software, and operations. The AWS Neuron team focuses on optimizing the performance of complex neural net models on our custom-built AWS hardware. Our dedicated group is developing a deep learning compiler stack that transforms neural network descriptions from frameworks such as TensorFlow, PyTorch, and MXNet into executable code. Together, we aim for a significant leap in performance for ML workloads. You:
In the role of Senior Machine Learning Compiler Engineer on the AWS Neuron team, you will spearhead essential features and initiatives for an advanced compiler designed for extensive ML workloads. Your responsibilities will include architectural design, publishing pioneering research, and mentoring passionate engineers. You will collaborate closely with AWS ML services teams and be involved in pre-silicon design, bringing innovative products and features to market, along with many other exciting projects. A background in Machine Learning and AI accelerators is advantageous but not mandatory. Basic Qualifications: 5+ years of professional software development experience. 5+ years of programming experience in at least one software language. 5+ years of experience in leading design or architecture of systems, focusing on reliability and scalability. 5+ years of involvement in the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations. Experience mentoring or leading tech teams. Preferred Qualifications: Bachelor's degree in computer science or equivalent. We are committed to fostering a diverse and inclusive work environment. Amazon is an equal opportunity employer, and we do not discriminate based on race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or any other legally protected status. Located in Cupertino, Seattle, Austin, or Toronto, this role has a salary range of $151,300 to $261,500, based on factors including location and experience. Our total compensation package may include equity, sign-on bonuses, and comprehensive benefits. This position will remain active until filled, and applicants should apply via our internal or external career site.