6 hours ago
Base Salary
$155k - $235k/yr
Responsibilities
- Design and build automation that diagnoses and resolves CI/CD pipeline failures.
- Develop methods to identify merges responsible for failures in long-running test suites.
- Evaluate AI techniques alongside deterministic automation to improve reliability.
- Architect scalable compute and storage for build and test workloads across cloud and on-premise environments.
- Own release engineering practices, including versioning, component release trains, and compatibility matrices.
- Reduce developer toil and time-to-signal across product, development, testing, and release workflows.
- Partner with development groups and Quality Engineering to drive adoption of tooling and best practices.
Requirements
- Demonstrated success building production-grade automation that reduced toil or time-to-signal in CI, release, or developer-tooling contexts.
- Release engineering experience with versioning schemes, independent component release trains, and compatibility matrices.
- Architectural judgment regarding AI versus deterministic software and compute availability versus data locality.
- Strong software engineering fundamentals and experience building production-grade automation rather than scripts.
- Experience driving adoption of tooling and best practices across teams without direct reporting relationships.
- Preferred experience with GitLab CI and merge-train workflows at scale.
- Preferred hands-on experience building LLM-based agents or developer workflow tools.
- Preferred experience with semantic versioning or independently versioned multi-component release systems.
- Preferred experience with CI/CD systems, containerized workloads, VMs, Kubernetes, distributed-systems observability, and systems administration.
Tech Stack
GitLab CI/CDKubernetes
Categories
About d-Matrix
d-Matrix builds AI inference computing platforms for data centers, combining custom silicon with systems, networking, and software. Its flagship Corsair platform and JetStream fabric focus on low-latency, energy-efficient generative AI inference at scale. Founded in 2019 and headquartered in Santa Clara, California, the privately held company sells hardware with accompanying software to cloud providers and enterprises deploying large AI models.