Software Dev Engineer, Ec2 Nitro

Amazon Amazon · Big Tech · Seattle, WA · Software Development

Software Development Engineer on the EC2 Nitro Machine Learning Systems team, focusing on building and optimizing performance measurement infrastructure for AI/ML workloads. The role involves establishing best-known configurations, translating performance insights into technical requirements, and analyzing training/inference performance across accelerated platforms. It requires expertise in low-level systems, ML frameworks, and serving layers.

What you'd actually do

  1. 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
  2. Develop comprehensive regression test coverage across all major component releases including frameworks, firmware, drivers, and networking technologies to maintain optimal platform performance
  3. 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
  4. Document and communicate performance insights to influence future platform designs by translating technical findings from research and customer workloads into actionable recommendations
  5. Identify and resolve complex performance challenges through systematic analysis of training and inference performance KPIs across accelerated platforms, working directly with customers to improve their ML system efficiency

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience programming with at least one software programming language
  • Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques

Nice to have

  • 3+ 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
  • Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT
  • Knowledge of machine learning model architecture and inference

What the JD emphasized

  • Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
  • Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT
  • Knowledge of machine learning model architecture and inference

Other signals

  • optimize the performance measurement infrastructure for some of the most computationally intensive AI/ML workloads on AWS
  • establish EC2 as the definitive source for best-known-configurations across diverse ML applications including LLMs, multimodal models, and video generation workloads
  • translating performance insights from state of the art research and customer workloads into technical requirements for upcoming accelerated platform launches
  • low-level systems (CUDA, EFA, firmware) through ML frameworks to serving layers
  • training and inference performance KPIs across accelerated platforms