Senior Software Engineer - AI/ML, AWS Neuron Inference
Amazon • Seattle, Washington, United States
No Relocation
Posted: May 18, 2026
Additional Content
Description
- AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machinelearning accelerators. This role is for a senior software engineer in the Machine Learning Inference Applications
Description
- AWS Neuron is the complete software stack for the AWS Inferentia and Trainium cloud-scale machine learning accelerators. This role is for a senior software engineer in the Machine Learning Inference Applications team. This role is responsible for development and performance optimization of core building blocks of LLM Inference - Attention, MLP, Quantization, Speculative Decoding, Mixture of Experts, etc. The team works side by side with chip architects, compiler engineers and runtime engineers to deliver performance and accuracy on Neuron devices across a range of models. Key job responsibilities Responsibilities of this role include adapting latest research in LLM optimization to Neuron chips to extract best performance from both open source as well as internally developed models. Working across teams and organizations is key. About the team Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future.
Basic Qualifications
- - 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience - Bachelor's degree in computer science or equivalent - 5+ years of programming using a modern programming language such as Java, C++, or C#, including object-oriented design experience - Fundamentals of Machine learning models, their architecture, training and inference lifecycles along with work experience on some optimizations for improving the model performance.