Applied Scientist, AWS Neuron Science Team

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.

Key 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. This role may also involve exposure to 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 includes an 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 Bedrock, Amazon Robotics, Amazon Ads, Amazon Rekognition and many more.

Basic Qualifications

• PhD in computer science, computer engineering, or related field
• Experience in patents or publications at top‑tier peer‑reviewed conferences or journals
• Experience in 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

Preferred Qualifications

• Experience investigating, designing, prototyping, and delivering new and innovative system solutions
• Experience in state‑of‑the‑art deep learning model architecture design and deep learning training, optimization, and model pruning
• Experience with popular deep learning frameworks such as MXNet and TensorFlow

Amazon is an equal‑opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/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.

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