System Development Engineer - EdgeAI, HW Compute Group
Confirmed on the employer's own hiring board on Oct 9, 2026. First seen by Alion on Oct 8, 2026. Amazon scores A on the Alion truth index.
Are you passionate about running large AI models directly on consumer devices - where every millisecond, milliwatt, and megabyte matters? Join our team to work at the core of the Neural Network Accelerator (NNA) software stack, driving on-device machine-learning inference, compiler and runtime development, and the automation infrastructure that ships production-quality AI features to millions of Amazon devices.
Key job responsibilities
As a SysDE I on the NNA / EdgeAI team, you will contribute to the software stack that compiles, deploys, and runs vision and language models on Amazon's in-house neural accelerators. You will work across the ML compilation pipeline (quantization, graph lowering, kernel selection, artifact packaging), the on-device inference runtime (secure and non-secure execution paths, memory and bandwidth budgets), and the release and validation infrastructure that keeps our device fleet healthy build over build.
A day in the life
You will help build and improve the test and evaluation systems that validate model accuracy, latency, memory footprint, and stability on physical devices in the lab - including large-scale evaluation of Vision-Language Models (VLMs) end-to-end from reference to device. You will also contribute to the build, release, and CI automation that keeps our multi-package software stack shipping cleanly across product platforms.
This role sits at the intersection of ML systems, embedded software, and release engineering.
Basic qualifications
- Bachelor's degree or above in computer science, computer engineering, or related field
- Experience working in a Linux/Unix environment
- Strong programming skills in one or more of C, C++, Python
- Experience with automating, building, testing, or deploying software
- Experience with CI/CD pipelines, build systems, and multi-package release engineering
Preferred qualifications
- Familiarity with machine learning fundamentals - model formats, quantization, inference vs training
- Experience with on-device or embedded ML runtimes, ML compilers, or accelerator toolchains
- Experience with device-side debugging: kernel logs, driver logs, ADB, on-device tracing
- Familiarity with cloud infrastructure (AWS) for large-scale test execution, log storage, and metrics
- Familiarity with agent-based / AI-assisted developer tooling for triage, code review, or release automation
- Minimum 3 year of experience in ML systems, embedded software, or release engineering
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