Principal Machine Learning Engineer

ServiceNow•Santa Clara, CA

2d•$240,100 - $420,200

About The Position

The Principal Machine Learning Engineer will define and build cloud-native infrastructure powering ServiceNow’s next generation of Agentic AI and conversational experiences. The role drives architecture and technical strategy across multiple engineering organizations while remaining hands-on with complex platform and scalability challenges. The engineer will lead the evolution of platform services, establish reusable architecture and engineering practices, and own technical initiatives from concept through production deployment. The role also provides technical leadership through influence, mentors senior engineers, and helps shape the future direction of ServiceNow’s Agentic AI platform.

Requirements

15+ years of experience building large-scale distributed systems, cloud platforms, infrastructure services, or enterprise SaaS products.
Deep expertise in Java, Go, Python, or similar systems programming languages.
Proven experience designing, building, and operating highly scalable cloud-native architectures.
Expert knowledge of Kubernetes, containers, orchestration platforms, and modern cloud infrastructure.
Experience building platform services used across multiple engineering teams and products.
Strong understanding of distributed systems principles, including resiliency, fault tolerance, scalability, performance optimization, and observability.
Experience with AWS, Azure, or Google Cloud at large scale.
Expertise in CI/CD, infrastructure automation, GitOps, Terraform, Helm, ArgoCD, or comparable technologies.
Strong software engineering fundamentals, including object-oriented design, system design, architecture patterns, and performance optimization.
Experience leading technical strategy and influencing architecture across multiple organizations without direct authority.
Demonstrated track record of building new platform capabilities from concept through large-scale production adoption.
Strong communication skills and ability to explain technical tradeoffs to engineering and executive leadership.

Nice To Haves

Experience building infrastructure for AI/ML, generative AI, agentic systems, or conversational platforms.
Experience supporting GPU workloads, model serving, inference platforms, or AI runtime environments.
Experience with internal developer platforms, platform engineering, SRE, or cloud infrastructure organizations.
Knowledge of multi-agent architectures, orchestration platforms, workflow engines, or large-scale automation systems.
Experience with observability platforms such as OpenTelemetry, Prometheus, Grafana, Datadog, or equivalent technologies.
Experience modernizing legacy architectures into cloud-native distributed services.

Responsibilities

Lead the technical direction, architecture, and evolution of cloud-native platform services supporting Agentic AI initiatives.
Drive the design and implementation of scalable Kubernetes-based infrastructure for AI/ML and conversational workloads at enterprise scale.
Partner with engineering, infrastructure, product, and platform teams to modernize critical services and transition to next-generation architectures.
Design and build highly available, secure, observable, and resilient distributed systems for mission-critical customer workloads.
Establish architectural standards, engineering best practices, and platform patterns for use across multiple product teams.
Drive automation across infrastructure provisioning, deployment pipelines, service operations, monitoring, and incident response.
Evaluate and adopt emerging technologies in cloud-native infrastructure, Kubernetes, AI platforms, and distributed systems.
Mentor senior engineers and provide technical leadership across organizational boundaries.
Lead architecture reviews and advise on scalability, reliability, performance, security, and operational excellence.
Partner with teams developing AI-powered experiences to ensure infrastructure supports future growth and innovation.
Work with organizational stakeholders to balance long-term platform investments with near-term product delivery goals.
Own technical initiatives from concept through production deployment and help shape the future direction of the Agentic AI platform.