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ServiceNow is seeking a Senior Staff Software Engineer to lead the design and delivery of complex components and services within their cloud-native platform. This is a hands-on technical leadership role focused on Kubernetes platform engineering, with significant responsibility for owning systems from design through production operation.
In this role, you will lead the design and delivery of complex components and services within the cloud-native platform, owning them from design through production operation. You'll act as the technical lead for major features and initiatives, translating ambiguous problems into clear, scalable, and executable designs. You'll partner with Principal and Distinguished Engineers to ensure alignment with broader platform architecture, engineering standards, and long-term technical strategy.
Key responsibilities include identifying technical risks early and driving them to resolution with a strong focus on reliability, scalability, operability, and engineering excellence. You'll maintain a strong hands-on presence across the development lifecycle, including designing, coding, and reviewing core systems such as Kubernetes operators, controllers, infrastructure automation, and platform services. Additionally, you'll mentor mid-level and junior engineers while raising the engineering bar through code reviews, design reviews, technical guidance, and collaborative problem-solving.
ServiceNow emphasizes an AI-native culture and is particularly interested in engineers who can think critically about integrating AI into platform and infrastructure work—including AI-powered tooling, automated operational workflows, and agentic systems for fleet visibility and operations.
REQUIREMENTS:
- 14+ years of experience designing, building, and operating large-scale distributed systems and cloud-native platforms in production environments
- Demonstrated experience building and operating Kubernetes platform infrastructure at scale, with ownership of platform capabilities through deployment, upgrades, and production operations
- Hands-on development of Kubernetes Operators, Admission Controllers, or other platform extensions using Kubebuilder, Operator Framework, or similar technologies
- Experience operating both managed Kubernetes services (EKS, AKS, GKE) and self-managed Kubernetes environments
- Experience supporting cloud-native database platforms, large-scale data infrastructure, or stateful services on Kubernetes
- Strong software engineering expertise in Go, with hands-on experience developing Kubernetes Operators, Controllers, Custom Resource Definitions (CRDs), and platform automation capabilities
- Deep understanding of Kubernetes architecture, including orchestration, scheduling, security, storage, networking, and workload management
- Proven experience operating stateful and distributed workloads on Kubernetes, including databases, data services, or other mission-critical platform components
- Extensive experience with public cloud platforms (AWS, Azure, or GCP), including compute, networking, security, and identity management services
- Strong background designing and building microservices, event-driven systems, and distributed application architectures
- Experience owning complex technical initiatives end-to-end, from architecture and design through implementation, deployment, and operational support
- Hands-on experience with containers, CI/CD, GitOps practices, and Infrastructure-as-Code using tools such as Terraform, Crossplane, or equivalent technologies
- Experience implementing observability and reliability best practices, including metrics, logging, tracing, monitoring, alerting, and service-level objectives
- Proven ability to drive technical strategy through architectural design reviews, technical proposals, and cross-functional collaboration
- Experience leveraging or critically thinking about how to integrate AI into engineering and platform work—AI-powered tooling, automated operational workflows, agentic systems for fleet visibility and operations, or reasoning about AI's impact on how infrastructure is built and run
Good to have:
- Experience with container networking (CNI) and/or service mesh
- Experience with mTLS, workload identity, and secrets management at fleet scale
- Experience with observability—metrics, tracing, and SLOs
- Experience with Terraform, Crossplane, or similar declarative infrastructure tooling
- Experience designing or tuning distributed data or compute workloads on Kubernetes
