Distinguished Engineer

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, Service Now is the AI control tower for business reinvention. Our Service Now 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.

** Cloud Platform - Kubernetes, Hyperscalers & Distributed Systems*
* Org-wide technical authority. Sets direction across multiple engineering organizations and owns the hardest, most ambiguous problems in the platform domain.

What you get to do in this role:

-    You will set the technical direction for our cloud-native platform across multiple engineering teams and organizations, defining the architecture and standards for how Kubernetes, distributed systems, and hyperscaler infrastructure are built and operated at scale.

-    You will own the hardest, most ambiguous technical problems in the platform domain - multi-cloud topology, control-plane design, workload isolation, identity and trust fabric, and reliability at the scale of hundreds of clusters and dozens of product workloads.

-    You will partner with engineering fellows, principal engineers, and other senior technical leaders to drive consistent architectural decisions and the adoption of best practices across the entire platform ecosystem.

-    You will identify and mitigate the deepest technical risks in initiatives with C-suite visibility, and you will be the person leadership trusts to make the call on managed-vs-self-managed tradeoffs, substrate portability, and multi-hyperscaler strategy.

-    Where you see the need, you will personally design and build the critical components - control planes, operators, infrastructure abstractions, and the systems other teams build on top of.

-    You will mentor staff and principal engineers and shape the next generation of the organization's technical leadership.

** To be successful in this role you have:*
* -    Experience leveraging or critically thinking about how to integrate AI into engineering and platform work - whether using AI-powered tooling, automating operational workflows, building agentic systems for fleet visibility and operations, or reasoning about AI's impact on how infrastructure is built and run.

-    12+ years of experience designing, building, and operating large-scale distributed systems in production, with deep expertise running Kubernetes at scale (multi-cluster, multi-region, multi-tenant).

-    Hands-on, authoritative experience across one or more major hyperscalers (AWS, Azure, GCP), including their managed Kubernetes offerings (EKS, AKS, GKE) and the networking, IAM, and capacity tradeoffs that come with each.

-    A proven track record of providing technical leadership across multiple engineering teams, influencing architecture and direction without relying on positional authority.

-    Deep expertise in the core building blocks of a modern platform: the operator/controller pattern, infrastructure-as-code and control planes (e.g., Crossplane), Git Ops-based delivery, container networking (CNI), and service mesh.

-    Strong programming skills in Go and fluency across the cloud-native ecosystem.

** It also helps if you have:*
* -    Experience designing identity and trust fabrics for distributed systems - workload identity, mTLS, and standards such as SPIFFE/SPIRE.

- Experience with multi-tenant workload isolation and runtime security (e.g., Kata Containers, sandboxed runtimes).

-    Experience building and operating platforms for regulated or federal markets (FedRAMP, air-gapped or self-hosted distribution, OCI bundling).

- Experience with observability at scale - metrics, tracing, and SLO-driven operations across a large fleet.

-    A platform-as-product mindset: treating internal engineering teams as customers and the platform as a…