Staff Engineer, Site Reliability Engineering

Vacancy Status:

Yes - This posting is for an existing vacancy within the organization and is open to new applications. (Backfill)

AI Disclosure:

As part of the application process, Artificial Intelligence will be used in the hiring process for this role

Work Arrangement: Hybrid: This role is categorized as hybrid. This means the successful candidate is expected to report to Markham office three times per week, at minimum.

About the role  

General Motors is transforming the automotive landscape through its next-generation Software-Defined Vehicle platform. Data is central to that transformation, powering safety, personalization, energy optimization, operational decision-making, and connected customer experiences. 

We are seeking a Staff Engineer to help make GM’s data platforms reliable, observable, operable, and scalable. This is a senior technical leadership role for someone who can move comfortably between system-level design, production operations, incident response, automation, and customer partnership. 

You will help define and spread the engineering patterns that make services easier to operate. You will work with SRE, data engineering, infrastructure, developer experience, application, and product teams to improve reliability from design through production and continuously improve how the organization operates. 

What you’ll do

• Lead the design and implementation of scalable, fault-tolerant, and observable infrastructure supporting vehicle telemetry, data ingestion, and platform operations. 

• Lead production readiness efforts across multiple teams—engaging directly in code, shaping reliability standards, guiding architectural improvements, and ensuring applications launch with resilient deployments, strong observability, and predictable operations. 

• Design, implement, and improve CI/CD delivery pipelines that make releases repeatable, safe, observable, and fast. Establish appropriate quality gates, artifact promotion, deployment verification, progressive delivery, and rollback practices. 

• Partner across SRE, product, and application teams to design and implement meaningful SLOs, SLIs, observability, monitoring and alerting, runbooks, and operational best practices. 

• Build and improve reusable AI workflows, skills, and evaluations. Apply appropriate validation techniques, including regression testing, structured evaluations, and LLM-as-a-judge approaches where useful. 

• Automate operational work, including incident intake, triage, diagnostics, remediation, evidence collection, service requests, and customer-facing status workflows. 

• Participate in a weekly on-call rotation with 12-hour shifts; the rotation cycles every eight weeks. Lead incident response, communicate clearly under pressure, and coordinate effective mitigation and recovery. 

• Participate in post-incident reviews and drive durable, system-level fixes that prevent recurrence rather than relying on short-term patches or repeated manual workarounds. 

• Partner directly with internal customers to understand their needs, explain technical trade-offs, and improve service outcomes with tact, empathy, and clear communication. 

• Influence technical direction across teams, mentor engineers, and raise engineering standards through design reviews, code reviews, documentation, and hands-on leadership with cross-functional engineering projects. 

• Balance reliability, performance, security, delivery speed, and cost when making technical decisions—especially under pressure. 
   

What you bring  

• 8+ years in SRE, DevOps, or systems engineering, including experience managing or mentoring high-impact teams. 

• Track record of designing and building and maintaining high-scale, cloud-native systems in production (preferably Azure, AWS, or GCP). 

• Hands-on experience architecting observability patterns, including standardized instrumentation, OTEL collector configuration, SLO/SLI definitions, and deploying observability resources like monitors, alerts, and dashboards. 

• Strong understanding of production readiness, service ownership, SLOs, incident management, post-incident learning, and continuous reliability improvement. 

• Experience participating in an on-call rotation and leading technical response to production incidents. 

• Experience designing, operating, and improving CI/CD pipelines. Understanding of GitOps, release strategies, quality gates, deployment verification, progressive delivery, and safe rollback is expected. 

• Strong programming ability in Python, Go, Java, or a comparable language, with disciplined code review, version control, testing, and maintainability practices. 

• Familiarity with AI-assisted software development, LLM application practices, agentic workflows, and evaluation techniques. 

• Ability to work effectively with internal customers, including in difficult or high-pressure situations, with professionalism, tact, and empathy. 

• Excellent ownership attitude and the ability to operate with pace, judgment, and accountability in a high-velocity environment. 

• Ability to influence without relying on formal authority and to increase adoption of shared engineering patterns. 

• Strong written and verbal communication skills for both technical and non-technical audiences. 

• BS / MS / PhD in computer science, engineering, physics, mathematics, or another relevant, technical field 

Preferred experience  

• Azure Databricks 

• Azure Event Hubs 

• Azure Kubernetes Service (AKS) 

• Kubernetes configuration management with Helm and Kustomize 

• Infrastructure as code, especially Terraform 

• GitHub Actions, Argo CD, and GitOps-based deployment models 

• Prometheus, Grafana, Datadog, OpenTelemetry, or comparable observability platforms and tools 

• LLM application development and testing with Promptfoo, agentic workflows and reusable AI skills with CoPilot 

• Experience operating large-scale data ingestion, processing, and delivery systems such as Fivetran, Apache Flink, Kafka, and Pulsar 

• Experience with vehicle telemetry, connected-vehicle platforms, or other high-volume event-driven systems