Company Description
It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.
Join us to put AI to work for people.
Job Description
What you get to do in this role:
- Design, build, and operate cloud-native engineering platforms for software validation, release validation, and production readiness
- Design and maintain production-like release and test ServiceNow environments that improve release confidence and deployment readiness.
- Build and integrate automated test pipelines, observability, reliability signals, deployment intelligence, and quality gates into CI/CD workflows.
- Develop automation solutions that improve engineering productivity, streamline operations, and reduce manual toil through shift-left engineering practices.
- Build reusable frameworks, self-service engineering environments, test data management, mock services, and developer productivity tooling.
- Design and enhance Kubernetes-based platforms supporting scalable test infrastructure, release automation, cloud-native workloads, and developer self-service.
- Implement automated validation for failure detection, deployment verification, policy enforcement, security checks, resilience testing, and operational health assessments.
- Resolve complex platforms, infrastructure, and networking challenges through software engineering, systems design, and automation.
- Partner closely with engineering teams to improve platform reliability, release quality, cloud-native adoption, and engineering best practices.
- Participate in architecture reviews, technical design discussions, and implementation of scalable, automation-first engineering solutions.
- Influence technical decisions through strong engineering execution, collaboration, and delivery of high-quality platform capabilities.
- Mentor engineers through technical guidance, code reviews, knowledge sharing, and engineering best practices.
- Foster a culture of reliability, automation, operational excellence, continuous improvement, and customer-focused engineering.
Qualifications
- Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
- 8+ years of experience in Site Reliability Engineering (SRE), DevOps, Platform Engineering, Software Engineering, or Infrastructure Engineering with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years experience; or equivalent experience.
- Hands-on experience with Kubernetes across cluster operations, networking, storage, security, autoscaling, and multi-cluster environments.
- Experience building and operating cloud-native platforms supporting scalable, highly available services.
- Experience integrating Kubernetes with CI/CD, GitOps, automated test pipelines, deployment validation, and cloud-native deployment workflows.
- Experience designing and implementing automation to improve developer productivity, release quality, and operational efficiency.
- Experience with progressive delivery practices, including canary deployments, feature flags, automated rollback, and deployment verification.
- Experience with chaos engineering, resilience testing, disaster recovery, and reliability validation.
- Strong software engineering skills with hands-on experience designing, developing, testing, and debugging applications using Python, Go, Java, or Ruby.
- Experience leveraging AI-assisted engineering for intelligent testing, release risk analysis, incident diagnostics, or operational automation is a plus.
- Strong understanding of observability, monitoring, SLI/SLOs, incident management, and production operations for distributed systems.
- Demonstrated ability to solve complex technical problems, drive projects independently, and collaborate effectively across engineering teams.
- Thrives in fast-paced, ambiguous environments with a strong ownership mindset, bias for action, and a passion for continuous learning and automation.
- Low ego, intellectually curious, and an effective collaborator who enjoys partnering with globally distributed teams to deliver reliable engineering solutions.
Good to have:
- Experience with observability and monitoring platforms for applications, services, and distributed systems at scale.
- Experience with DevOps automation, CI/CD pipelines, GitOps, and Agile development practices using tools such as GitLab CI/CD, Argo CD, or Flux.
- Experience building and maintaining enterprise-scale test automation frameworks using technologies such as Playwright, Selenium, Cypress, REST Assured, PyTest, JUnit/TestNG, or equivalent.
- Experience with test orchestration, intelligent regression testing, test impact analysis, flaky test detection, parallel execution, and test data management.
- Experience with service virtualization, contract testing, synthetic testing, and building developer self-service engineering platforms.
- Experience with Infrastructure as Code and configuration management tools such as Ansible, Terraform, or equivalent.
- Experience with the Kubernetes ecosystem, including Helm, Argo Workflows, Kustomize, Istio/Linkerd, Gateway API/Ingress, Prometheus, OpenTelemetry, and container runtime technologies.
- Experience operating Kubernetes platforms across public cloud providers, including AWS (EKS), Azure (AKS), and Google Cloud (GKE).
- Experience implementing progressive delivery practices, including canary deployments, feature flags, deployment verification, and automated rollback.
- Familiarity with AI-assisted engineering, intelligent testing, operational automation, or cloud-native engineering platforms.
Additional Information
Work Personas
We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.
Equal Opportunity Employer
ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.
Accommodations
We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance.
Export Control Regulations
For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.
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