Staff Machine Learning Engineer

Qualifications

• To be successful in this role you have:
• 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.
• 8+ 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 developing 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.
• 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.
• 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.
• Distributed Systems Expertise
• Experience with large‐scale deployments of distributed systems.
• Deep knowledge of replication, fault-tolerant, and HA strategies.
• Experience working in DevOps environments to operationalize distributed platforms.
• Strong expertise in designing and architecting platforms using:
• Kubernetes (workload deployments, upgrades, monitoring and maintenance)
• Apache Kafka (high‐scale clusters, topic/partition strategies, HA) is a plus
• Redis (high‐scale clusters, HA) is a plus
• Expertise in data formats such as Parquet, ORC, and Avro, along with compaction and governance strategies.
• Ability to build scalable, fault‐tolerant ingestion and transformation workflows.
• 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.
• 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. • 8+ 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 i