Originally posted 15 October 2026 by the employer.
This role defines and builds how d-Matrix uses AI-enabled automation across the software development lifecycle for its kernel compiler, firmware, device drivers, and inference serving stack.
About the role
This role defines and builds how d-Matrix uses AI-enabled automation across the software development lifecycle, from build and CI through testing and release, for its kernel compiler, firmware, device drivers, and inference serving stack.
What you'll do
- Set the technical direction for AI-enabled workflow automation across build, CI, test, and release. - Build the shared foundation, including the agent harness and tool integrations into GitLab, Jira, test results, logs, and observability data. - Develop evaluation methodology to measure whether an automation saves time and preserves correctness. - Define guardrails and standards for where AI acts autonomously, where it proposes and a human decides, and how every change stays auditable. - Deliver the first Quality Engineering automations, including cross-layer failure attribution, failure clustering and defect deduplication, coverage-gap analysis, and release-readiness summaries. - 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.
What you'll need
- Extensive production software engineering experience, with strong Python skills. - Rust and/or C++ experience to understand build flows and diagnose compiler errors. - Experience shipping AI-enabled tools or automation that others relied on in production. - Deep hands-on experience with LLM APIs, agent harnesses, tool integration, and context engineering. - Experience designing evaluations for nondeterministic systems, showing that an automation helped without breaking something else. - Strong fluency across the development lifecycle, including CI/CD, testing, and release processes.
Nice to have
- 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.
Skills: AI-enabled workflow automation, agent harness, LLM APIs, CI/CD, testing, and release processes, Quality Engineering automations
This role has been open 0 days — well below the 50-day median for Infrastructure/Platform Software roles.
Infrastructure/Platform Software · Infrastructure Platform
|
Open roles in category
973
|
Median days open
50 d
|
Median salary
$235k
|
See the full market breakdown ▾Category comparison, skills in demand, and who else is hiring
| Metric | d-Matrix | All employers we track in this specialty (973 roles · 85 employers) |
|---|---|---|
| Open roles in this specialty | 12 | 973 |
| Open roles in the wider Software, Firmware & Systems family | 23 | 6127 · 145 employers |
| Median days open | 14 d | 50 d (−36 d vs this employer) |
| Median salary (USD postings) | — | $235k |
Skills observed across this category: AI-enabled workflow automation, agent harness, LLM APIs, CI/CD, testing, and release processes, Quality Engineering automations
Who's hiring in this category
- NVIDIA · 289 open roles · median 44 d
- Qualcomm · 61 open roles · median 57 d
- AMD · 57 open roles · median 21 d
- Graphcore · 44 open roles · median 90 d
- Intel Corporation · 40 open roles · median 9 d
- Cerebras · 39 open roles · median 80 d
How we counted: 973 open Infrastructure/Platform Software (Infrastructure Platform) roles from 85 employers tracked in the SemiconductorJobs index, counted 15 Oct 2026. Specialty figures count only roles carrying this exact specialty label, so an employer's related work in neighbouring specialties is not included there — it is counted in the wider Software, Firmware & Systems family row. Figures refresh nightly.