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d-Matrix is hiring a Principal Test Automation Engineer to define and build AI-enabled automation across the software development lifecycle, from build and CI through testing and release. The company designs custom silicon and operates a complex codebase spanning kernel compilers, firmware, device drivers, and an inference serving stack. Tests require real hardware, correctness is numerical rather than binary pass/fail, and failures can originate across multiple layers.
In this role, you will set the technical direction for AI-enabled workflow automation and build the foundational systems that other engineers depend on. You will design and implement the agent harness, integrations into GitLab, Jira, test results, and logs, establish guardrails for autonomous vs. human-in-the-loop AI actions, and develop evaluation methodologies to measure whether automations genuinely save time while preserving correctness. You will deliver the first Quality Engineering automations, including cross-layer failure attribution, failure clustering and defect deduplication, coverage-gap analysis, and release-readiness summaries.
You will stay hands-on and ship the hardest pieces yourself, working alongside a Senior/Staff engineer in the same area. You will raise the bar for how the broader engineering organization uses AI in its daily work through shared patterns, design reviews, and mentoring. You will determine when an agent, a deterministic tool with a narrow model step, or a simple script is the right solution, and teach that judgment to others.
REQUIREMENTS:
- Extensive production software engineering experience with strong Python skills
- Rust and/or C++ experience sufficient to understand build flows and diagnose compiler errors
- Demonstrated experience shipping AI-enabled tools or automation that others relied on in production (beyond demos)
- Deep hands-on experience with LLM APIs, agent harnesses, tool integration, and context engineering
- Experience designing evaluations for nondeterministic systems, demonstrating that an automation helped without breaking something else
- Sound judgment about when AI is the right tool, including examples of choosing not to use it
- Strong fluency across the development lifecycle, including CI/CD, testing, and release processes
- Track record of setting technical direction across teams that do not report to you
PREFERRED QUALIFICATIONS:
- Exposure to compilers, firmware, or hardware-in-the-loop test environments
- Experience building tool integrations with MCP or similar protocols
- Experience working with GitLab, Jira, or Prometheus APIs
- Experience managing cost and latency in LLM-based systems
- Daily use of AI coding tools such as Claude Code, Cursor, or Copilot, with well-formed opinions on where they help and where they do not
