System Development Engineer - EdgeAI, HW Compute Group

About the job

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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