Amazon
Software Development Engineer, AWS
$144K–$224K
New York City, NY +7 · Full-time · Posted Aug 17, 2026
- Location
- New York City, NY
Arlington, VA
Seattle, WA
New York, NY
Astoria, NY
Brooklyn, NY
Carle Place, NY
Cupertino, CA - Experience
- 3+ years of non-internship professional software development; 2+ years of non-internship system design or architecture; 3+ years building complex software systems; 2+ years designing and developing large-scale software using C#, C++, Java, or Perl.
- Education
- Bachelor's degree or equivalent.
About the job
Develops and maintains software across AWS teams, designing and shipping solutions, resolving bugs and performance issues, and contributing to system architecture and engineering practices.
What you'll do
- Design, develop, and maintain reliable, reusable software; implement solutions that meet business requirements.
- Collaborate across teams to define, design, and deliver features; participate in architecture and design reviews.
- Troubleshoot performance bottlenecks and bugs, improve processes, and mentor junior engineers.
What they ask for
- 3+ years of non-internship professional software development experience.
- 2+ years of non-internship experience in design or architecture of new and existing systems, including design patterns, reliability, and scaling.
- 3+ years of experience building complex software systems.
- 2+ years designing and developing large-scale applications, tools, systems, or services using C#, C++, Java, or Perl.
Nice to have
- Knowledge of ML frameworks including JAX, PyTorch, vLLM, SGLang, Dynamo, TorchXLA, and TensorRT.
- Knowledge of machine learning and LLM fundamentals, and Python and/or C++ programming.
Pay
USD 165,200.00 - 223,600.00 annually (base salary); USD 158,100.00 - 213,800.00 annually (base salary); USD 143,700.00 - 194,400.00 annually (base salary)
Tools
C#, C++, Java, Perl, JAX, PyTorch, vLLM, SGLang
AI focus
Some anticipated roles involve AI/ML and healthcare AI; preferred knowledge includes ML frameworks and machine learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques.
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