> Markdown version of [/jobs/ext/4691423-applied-scientist-aws-neuron-science-team](https://www.wearedevelopers.com/jobs/ext/4691423-applied-scientist-aws-neuron-science-team). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied Scientist, AWS Neuron Science team - **Company:** Amazon.com, Inc. - **Location:** Cupertino, CA, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Code Generation, Computer Programming, Linux Kernel, Machine Learning, Performance Tuning, AWS Trainium, Machine Learning Operations - **Published:** November 12, 2026 - **Apply:** https://www.careerbuilder.com/job-details/applied-scientist-aws-neuron-science-team-cupertino-ca--6508c474-b81e-437c-87c7-dba3c98e7229 ## About the Role Amazon Web Services (AWS), Customer Acquisition, Amazon Web Services (AWS), Academic Research, Environmental Impact, Kernel Programming, Artificial Intelligence (AI), Machine Learning, Performance Tuning/Optimization, Cloud Computing ## Description The AWS Neuron Science Team is looking for talented scientists to enhance our software stack, accelerating customer adoption of Trainium and Inferentia accelerators. In this role, you will work directly with external and internal customers to identify key adoption barriers and optimization opportunities. You"ll collaborate closely with our engineering teams to implement innovative solutions and engage with academic and research communities to advance state-of-the-art ML systems. As part of a strategic growth area for AWS, you"ll work alongside distinguished engineers and scientists in an exciting and impactful environment. We actively work on these areas: * AI for Systems: Developing and applying ML/RL approaches for kernel/code generation and optimization * Machine Learning Compiler: Creating advanced compiler techniques for ML workloads * System Robustness: Building tools for accuracy and reliability validation * Efficient Kernel Development: Designing high-performance kernels optimized for our ML accelerator architectures