Software Dev Engineer, EC2 Nitro
Amazon - Seattle, Washington, USA
Posted Apr 20, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Mental health support
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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Where they hire
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About this role
Software Dev Engineer, EC2 Nitro Seattle, Washington, USA Join the EC2 Nitro Machine Learning Systems team to revolutionize accelerated computing in the cloud. We're seeking an exceptional Software Development Engineer to build and optimize the performance measurement infrastructure for some of the most computationally intensive AI/ML workloads on AWS. In this role, you'll establish EC2 as the definitive source for best-known-configurations across diverse ML applications including LLMs, multimodal models, and video generation workloads. Your expertise will directly influence future platform designs by translating performance insights from state of the art research and customer workloads into technical requirements for upcoming accelerated platform launches. Your impact will extend from low-level systems (CUDA, EFA, firmware) through ML frameworks to serving layers, requiring deep technical knowledge and the ability to communicate complex performance data as actionable business insights. This position offers the unique opportunity to shape the future of machine learning infrastructure at cloud scale while working at the intersection of high-performance computing, distributed systems, and machine learning technologies. Key job responsibilities - Design and build foundational infrastructure for ML performance measurement that scales with business demand and operates as reliable CI/CD systems, ensuring high-quality implementations that balance customer requirements with operational excellence - Develop comprehensive regression test coverage across all major component releases including frameworks, firmware, drivers, and networking technologies to maintain optimal platform performance - Collaborate with cross-functional teams to establish EC2 as the definitive source for best-known-configurations across diverse ML applications including LLMs, multimodal models, and MoE architectures - Document and
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