Overview
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 keep
Full job description
Full Job Description
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
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Requirements & 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
Tips for this job
Practical JobOpportunity guidance. These tips do not replace official rules or create new eligibility requirements.
- Tailor the CV and application to the responsibilities and required skills stated on the official employer page.
- Use concrete evidence of relevant work, projects and measurable results rather than generic claims.
- Confirm location, work authorization, remote restrictions and sponsorship terms before applying.
- Apply through the original employer or official recruitment destination shown on this page.
JobOpportunity.info helps you discover and organize source listings. Confirm eligibility, dates, salary/funding and application instructions on the original source before submitting anything.
Apply through JobOpportunity →Browse current JobOpportunity listings from Amazon Com Opportunities →
