Role OverviewSoftware Infrastructure owns the CI/CD and release pipeline that every d-Matrix engineer ships through. As a Staff-level individual contributor, you will drive the automation that diagnoses and fixes pipeline failures, such as deciding whether a failed CI job is a real regression, a flaky card, or an infrastructure issue, and identifying which merge broke an overnight test run. Rapid company growth and AI-accelerated development are increasing development velocity, and the pipeline from product through dev, test, and release must keep pace.
You will blend modern AI techniques with traditional software automation to build highly reliable systems, and shape the architecture for compute and storage across an environment that spans cloud to on-prem, from firmware updates to rack-scale performance testing. You will partner with the Software Infrastructure team, development groups, and Quality Engineering to develop and spread best practices across the organization.
This role is not primarily an AI agent role, and it is not a DevOps role. Kubernetes and systems skills are valued but are not the focus.
What You Will Do- Design and build automation that diagnoses and resolves CI/CD pipeline failures, including classification of regressions, flaky hardware, and infrastructure issues.
- Develop automated methods to identify which merges cause failures in long-running test suites.
- Evaluate and integrate AI techniques alongside deterministic automation to maximize reliability.
- Architect scalable compute and storage for build and test workloads across cloud and on-prem environments.
- Own release engineering practices, including versioning schemes, independent component release trains, and compatibility matrices.
- Reduce developer toil and time-to-signal across the product, dev, test, and release pipeline.
- Partner with development groups and Quality Engineering to drive adoption of new tooling and best practices.
What You Will Bring- Track record of building automation that measurably reduced toil or time-to-signal in a CI, release, or dev-tooling context.
- Release engineering experience, including versioning schemes, independent component release trains, and compatibility matrices.
- Architectural judgment on trade-offs such as AI versus deterministic software, and compute availability versus data locality.
- Strong software engineering fundamentals, with experience building production-grade automation rather than scripts.
- Track record of driving adoption of new tooling and best practices across teams that do not report to you.
Preferred Qualifications- Experience with GitLab CI and merge-train workflows at scale.
- Hands-on experience building LLM-based agents or tools for developer workflows, such as CI triage or code review.
- Experience with semantic versioning or independently versioned multi-component release systems.
- Experience as a user of CI/CD systems, with an understanding of the developer-customer perspective.
- Hands-on experience with containerized workloads, VMs, and Kubernetes.
- Experience with observability of distributed systems, including metrics, logging, reporting, and alerting.
- Systems administration skills for investigating host-level issues.
