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d-Matrix is seeking a Staff Software Engineer to own the CI/CD and release pipeline infrastructure that powers the company's development velocity. As a Staff-level individual contributor, you will drive automation that diagnoses and resolves pipeline failures—determining whether a failed CI job represents a real regression, a flaky test, or an infrastructure issue, and identifying which merge broke overnight test runs.
You will blend modern AI techniques with traditional software automation to build highly reliable systems and shape the architecture for compute and storage across a hybrid environment spanning cloud to on-premises, 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.
Key responsibilities include:
- 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
This role is not primarily an AI agent role and is not a DevOps role. Kubernetes and systems skills are valued but are not the focus.
REQUIREMENTS:
- 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
