Test Automation Engineer
US$195,000 – US$285,000 / yr base salary
Posted 15 Oct 2026
Advertised as “Principal Test Automation Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
| Base salary | US$195,000 – US$285,000 / yr |
|---|
At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration.
We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? Together, we can help shape the endless possibilities of AI.
Principal Test Automation Engineer
Location: Santa Clara, Ca / Remote, US
Role Overview
d-Matrix is hiring a Principal Software Engineer to define and build how we use AI-enabled automation across the software development lifecycle, from build and CI through testing and release. Our codebase spans a kernel compiler, firmware, device drivers, and an inference serving stack running on silicon we designed ourselves. Tests require real hardware, correctness is numerical rather than pass/fail, and failures can originate in any of several layers. You will set the technical direction for AI-enabled workflow automation and build the foundations other engineers rely on, including the agent harness, integrations into GitLab, Jira, test results, and logs, guardrails, and evaluation methods that show whether an automation actually helped. You will stay hands-on and ship the hardest pieces yourself, working alongside a Senior/Staff engineer in the same area while raising the bar for how the broader engineering organization uses AI in its daily work.
What You Will Do
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Set the technical direction for AI-enabled workflow automation across build, CI, test, and release.
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Build the shared foundation, including the agent harness and tool integrations into GitLab, Jira, test results, logs, and observability data.
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Develop evaluation methodology to measure whether an automation saves time and preserves correctness.
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Define guardrails and standards for where AI acts autonomously, where it proposes and a human decides, and how every change stays auditable.
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Deliver the first Quality Engineering automations, including cross-layer failure attribution, failure clustering and defect deduplication, coverage-gap analysis, and release-readiness summaries.
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Raise the bar across engineering through shared patterns, design reviews, and mentoring.