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ServiceNow is seeking a Senior Staff Machine Learning Engineer to lead the design and delivery of complex components and services within their cloud-native platform. In this role, you will own systems from design through production operation, acting as a technical lead for major features and initiatives.
Key responsibilities include:
- Leading the design and delivery of complex cloud-native platform components with end-to-end ownership
- Serving as technical lead for major features, translating ambiguous problems into clear, scalable designs
- Partnering with Principal and Distinguished Engineers to ensure alignment with platform architecture and long-term technical strategy
- Identifying and resolving technical risks with focus on reliability, scalability, and operability
- Maintaining hands-on involvement across the development lifecycle: designing, coding, and reviewing core systems including Kubernetes operators, controllers, infrastructure automation, and platform services
- Mentoring mid-level and junior engineers, raising the engineering bar through code reviews, design reviews, and technical guidance
ServiceNow describes itself as an AI control tower for business reinvention, with an AI-native culture. The role emphasizes integrating AI into engineering and platform 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 through deployment, upgrades, and production operations
- Hands-on development of Kubernetes Operators, Admission Controllers, or 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, CRDs, and platform automation
- Deep understanding of Kubernetes architecture (orchestration, scheduling, security, storage, networking, workload management)
- Proven experience operating stateful and distributed workloads on Kubernetes, including databases and mission-critical platform components
- Experience with public cloud platforms (AWS, Azure, GCP) including compute, networking, security, and identity management
- Strong background designing and building microservices, event-driven systems, and distributed application architectures
- Experience owning complex technical initiatives end-to-end from architecture through operational support
- Hands-on experience with containers, CI/CD, GitOps practices, and Infrastructure-as-Code (Terraform, Crossplane, or equivalent)
- Experience implementing observability and reliability best practices (metrics, logging, tracing, monitoring, alerting, SLOs)
- Proven ability to drive technical strategy through architectural design reviews and cross-functional collaboration
- Experience leveraging or critically thinking about how to integrate AI into engineering and platform work
Desirable qualifications:
- Container networking (CNI) and/or service mesh experience
- mTLS, workload identity, and secrets management at fleet scale
- Terraform, Crossplane, or similar declarative infrastructure tooling
- Designing or tuning distributed data or compute workloads on Kubernetes
- Large-scale deployments of distributed systems with deep knowledge of replication, fault-tolerance, and HA strategies
- DevOps experience operationalizing distributed platforms
- Apache Kafka (high-scale clusters, topic/partition strategies, HA)
- Redis (high-scale clusters, HA)
- Data formats (Parquet, ORC, Avro) with compaction and governance strategies
- Building scalable, fault-tolerant ingestion and transformation workflows
