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Salary: USD 240,100 - 420,200 / annual
ServiceNow is seeking a Principal Machine Learning Engineer to define and build the cloud-native infrastructure powering its next-generation Agentic AI and conversational experiences. In this role, you will drive technical architecture and strategy across multiple engineering organizations while remaining deeply hands-on in solving complex platform and scalability challenges.
Key Responsibilities:
- Lead technical direction, architecture, and evolution of cloud-native platform services supporting Agentic AI initiatives
- Design and implement scalable Kubernetes-based infrastructure supporting AI/ML and conversational workloads at enterprise scale
- Partner with engineering, infrastructure, product, and platform teams to modernize critical services and accelerate transition to next-generation architectures
- Design and build highly available, secure, observable, and resilient distributed systems supporting mission-critical customer workloads
- Establish architectural standards, engineering best practices, and platform patterns leveraged 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 through influence across organizational boundaries
- Lead architecture reviews and provide guidance on scalability, reliability, performance, security, and operational excellence
- Partner closely with teams developing AI-powered experiences to ensure infrastructure supports future growth and innovation
- Work with stakeholders to balance long-term platform investments with near-term product delivery goals
- Own technical initiatives from concept through production deployment while shaping 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 building new platform capabilities from concept through large-scale production adoption
- Strong communication skills with ability to explain technical tradeoffs to engineering and executive leadership
Preferred Qualifications:
- 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 modern observability platforms including OpenTelemetry, Prometheus, Grafana, Datadog, or equivalent
- Experience modernizing legacy architectures into cloud-native distributed services
