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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied Scientist, AWS Neuron Science Team - **Company:** Amazon.com, Inc. - **Location:** Santa Clara, CA, United States - **Salary:** $171,600.0 - $222,200.0 - **Contract:** Permanent contract - **Skills:** Mxnet, Java (Programming Language), Artificial Intelligence, Amazon Alexa, Amazon Web Services, C++ (Programming Language), Cloud Computing, Code Generation, Databases, Computer Engineering, Data Mining, Distributed Systems, Python (Programming Language), Linux Kernel, Machine Learning, Parsing, Productivity Software, Cloud Services, Tensorflow, Azure Machine Learning, High Performance Computing, Mobile Robots, Deep Learning, Information Technology, Machine Learning Operations, Databricks - **Published:** June 12, 2026 - **Apply:** https://dejobs.org/x/x/7E1B17B03A544D07ACCE7F769ADE5831/job/ ## About the Role * PhD in computer science, computer engineering, or related field * Experience in patents or publications at top-tier peer-reviewed conferences or journals * Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing * Experience programming in Java, C++, Python or related language * Experience using Unix/Linux, * Experience in investigating, designing, prototyping, and delivering new and innovative system solutions * Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning * Experience with popular deep learning frameworks such as MxNet and Tensor Flow ## 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 A day in the life AWS Utility Computing (UC) provides product innovations that continue to set AWS's services and features apart in the industry. As a member of the UC organization, you'll support the development and management of Compute, Database, Storage, Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Additionally, this role may involve exposure to and experience with Amazon's growing suite of generative AI services and other cloud computing offerings across the AWS portfolio. About the team AWS Neuron is the software of Trainium and Inferentia, the AWS Machine Learning chips. Inferentia delivers best-in-class ML inference performance at the lowest cost in the cloud to our AWS customers. Trainium is designed to deliver the best-in-class ML training performance at the lowest training cost in the cloud, and it's all being enabled by AWS Neuron. Neuron is a Software that include ML compiler and native integration into popular ML frameworks. Our products are being used at scale with external customers like Anthropic and Databricks as well as internal customers like Alexa, Amazon Bedrocks, Amazon Robotics, Amazon Ads, Amazon Rekognition and many more. ## Related Videos - [Making neural networks portable with ONNX](https://www.wearedevelopers.com/videos/301-making-neural-networks-portable-with-onnx) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Building a Compiler with C#](https://www.wearedevelopers.com/videos/116-building-a-compiler-with-c) ## Related Articles - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Dev Digest 162: AI careers, MCP, AWS best practices & floppy sweaters](https://www.wearedevelopers.com/magazine/571-dev-digest-162-ai-careers-mcp-aws-best-practices-floppy-sweaters)