General Motors
Staff Engineer, Site Reliability Engineering
Warren, Michigan, United States of America +2 · Hybrid · Staff · Full-time · Posted Oct 19, 2026
- Location
- Warren, Michigan, United States of America
Charlotte, North Carolina, United States of America
Austin, Texas, United States of America - Experience
- 8+ years in SRE, DevOps, or systems engineering; includes experience managing or mentoring high-impact teams.
- Education
- BS / MS / PhD in computer science, engineering, physics, mathematics, or another relevant, technical field
- Visa sponsorship
- GM does not provide immigration-related sponsorship for this role; applicants must not need GM sponsorship now or in the future.
About the job
Leads reliability engineering for GM’s data platforms, improving their design, deployment, observability, and production operations for connected-vehicle systems.
What you'll do
- Design scalable, fault-tolerant infrastructure and lead production readiness, reliability standards, and CI/CD improvements across teams.
- Develop observability, SLOs, and operational practices; lead incident response and drive lasting fixes through post-incident reviews.
- Automate operational workflows and build, validate, and improve reusable AI workflows; mentor engineers and influence cross-team technical direction.
What they ask for
- 8+ years in SRE, DevOps, or systems engineering, including experience managing or mentoring high-impact teams.
- Experience designing, building, and maintaining high-scale cloud-native production systems; hands-on observability, incident response, and on-call experience.
- Experience designing and improving CI/CD pipelines; strong programming ability in Python, Go, Java, or a comparable language.
- BS / MS / PhD in computer science, engineering, physics, mathematics, or another relevant, technical field.
Nice to have
- Azure Databricks, Azure Event Hubs, or Azure Kubernetes Service (AKS).
- Experience with Terraform, GitHub Actions, Argo CD, observability tools, LLM application development, or high-volume data systems.
Tools
Azure, AWS, GCP, Python, Go, Java, OpenTelemetry, Terraform
AI focus
Build and improve reusable AI workflows, skills, and evaluations; apply validation such as regression testing, structured evaluations, and LLM-as-a-judge approaches. Familiarity with AI-assisted development, LLM applications, and agentic workflows is requested.
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