Staff Quality Automation Engineer

Role overview

The Quality Engineering (QE) team tests, triages and reports deficits, then verifies fixes. It works with Product and Engineering to assess functional behavior, numerical accuracy, performance and stability.

As a Staff Quality Automation Engineer, you will turn QE workflows, automation and prototypes into reliable internal products used within QE and across d-Matrix. You will build reusable capabilities that improve how teams test and develop software. You will support test engineers and software engineers across d-Matrix in delivering robust product releases.

You already use AI in your engineering work and actively explore what new tools make possible. You will bring fresh approaches to QE and turn useful experiments into capabilities colleagues can adopt.

What you will do

• Work with QE and Engineering to understand users' needs and agree priorities. Define scope and expected results. Raise dependencies and blockers, and ask for help reaching the right owner.
• Build and maintain shared automation, test frameworks and harnesses for functional, integration, regression and release testing across supported software and hardware configurations.
• Turn scripts and prototypes into tools others can use. Provide clear interfaces, configuration, setup instructions, error handling and diagnostics. Review code and test the tools.
• Integrate tools with existing continuous integration (CI), nightly and release workflows alongside infrastructure owners. Record builds, configurations, commands, inputs and results so another engineer can reproduce failures.
• Support adoption through documentation, examples and user feedback. Check that colleagues can use tools independently. Measure human effort, reliability and result quality, including review and maintenance costs.
• Assess coverage, assertions, expected results and negative cases. Distinguish product deficits, environment problems and unreliable tests. Work with developers to verify fixes and provide testing cover while progressing shared automation.
• Keep up with advances in AI coding agents and workflow tools. Test promising approaches on real QE work, share what you learn and build on the ones that improve results. Use approved tools, check outputs and follow approved data-handling rules.

Initial outcomes — proposed first 90 days

• Understand one QE workflow, its users, dependencies and failure modes. Establish an improvement baseline.
• Deliver a reviewed automation improvement for an agreed need in the existing test environment.
• Show reliable repeat execution, useful diagnostics and reproducible results against identified builds and configurations.
• Have intended users set up, run and troubleshoot the tool from its documentation. Act on their feedback.
• Compare effort, reliability or result quality with the baseline. Hand over operating and maintenance notes.

Agree scope, targets and measurement windows with the manager after onboarding.

What you will bring — minimum qualifications

• 5+ years of experience
• Strong software-development skills: write, debug and maintain code, explain design choices, review others' work and investigate failures beyond a test's surface result.
• Experience building shared test automation, frameworks or engineering tools that others use repeatedly.
• Evidence of turning prototypes or manual workflows into reliable tools, including configuration, failure handling, documentation, adoption and maintenance.
• Practical test engineering: requirements, boundary and negative cases, coverage, and failure investigation using code, logs and reproducible experiments.
• Experience with Linux, version control, code review, automated tests and CI workflows.
• Regular use of AI coding agents or AI workflows, with examples of what you built, how your approach evolved and how you checked the results.
• Experience guiding technical work across distributed teams through design and code reviews. Explain tradeoffs, understand users' needs, make progress independently and raise missing decisions promptly.

Preferred qualifications

• Systems software testing in firmware, drivers, compilers, runtimes or distributed systems, including real devices.
• ML inference, model execution, numerical comparisons, quantization or performance benchmarks.
• Test frameworks, CI pipelines, containers, reporting or lab automation.
• Hardware-in-the-loop, multi-card or multi-node configurations, and extended stability or soak testing.
• Internal engineering products used across teams, or human-reviewed AI workflows used repeatedly.