Principal Software Engineer

ServiceNow•Santa Clara, CA

11d•$221,200 - $387,100•Hybrid

About The Position

Service Operations brings together IT Service Management (ITSM), IT Operations Management (ITOM), and CMDB capabilities on the ServiceNow Platform to help customers deliver resilient, intelligent, and efficient enterprise services. The organization connects incident, request, fulfillment, infrastructure visibility, service mapping, configuration data, and AIOps into a unified operating model. By combining service delivery with operational intelligence and trusted configuration data, Service Operations enables proactive, AI-assisted, and increasingly self-healing operations at enterprise scale. Together, these capabilities help customers modernize operations, improve service reliability, reduce manual effort, and drive measurable productivity outcomes on a single ServiceNow platform. As a Principal Software Engineer, you will play a key role in shaping the future of Service Operations as ServiceNow evolves toward AI-native products for the agentic era. This role is about helping define and execute a long-term technical vision for how enterprises deliver, operate, automate, and continuously improve services across increasingly complex digital environments. You will help build the foundational platforms, frameworks, and product capabilities that connect service delivery, operations intelligence, trusted configuration data, automation, and AI-powered workflows into a unified Service Operations experience. The work will span large-scale distributed systems, intelligent data collection and processing, microservices, platform architecture, operational intelligence, and AI-assisted user and enterprise workflows. This role requires deep technical expertise, strong architectural judgment, and the ability to translate long-term product vision into durable engineering execution. You will influence architecture across teams, raise engineering standards, mentor senior engineers, and help deliver industry-leading Service Operations capabilities that can scale and evolve over the next decade.

Requirements

Typically, 15+ years of related experience with a Bachelor’s degree; 12+ years with a Master’s degree; 8+ years with a PhD; or equivalent practical experience.
Strong experience building and operating large-scale distributed systems, platform services, enterprise applications, microservices, or cloud-native infrastructure.
Proven ability to define technical vision, influence architecture across teams, and translate long-term product strategy into durable engineering execution.
Experience designing systems for scale, reliability, observability, fault tolerance, data quality, security, and production readiness.
Strong technical depth in Java, Python, or similar object-oriented programming languages.
Strong knowledge of data structures, algorithms, object-oriented design, design patterns, API design, and performance optimization.
Experience with AI-powered products, AI-assisted workflows, automation platforms, or agentic systems is strongly preferred.
Ability to think critically about how AI can be integrated into enterprise workflows, decision-making, operations, and user experiences.
Experience validating AI-assisted systems, including data quality, model outputs, workflow correctness, inference consistency, evaluation patterns, or human-in-the-loop controls.
Experience with enterprise service management, IT operations, observability, CMDB, discovery, service mapping, AIOps, endpoint management, or operational intelligence is a strong plus.
Ability to troubleshoot complex systems and optimize performance across application, service, data, and infrastructure layers.
Familiarity with automated testing frameworks and experience integrating functional, regression, integration, scalability, and performance tests into CI/CD pipelines.
Experience with modern engineering tools such as IDEs, debuggers, profilers, source control systems, observability platforms, and Unix-based environments.
Ability to mentor senior engineers, raise engineering standards, and lead through technical credibility, collaboration, and influence.

Nice To Haves

Experience with modern front-end frameworks such as Angular, React, or Vue is a plus, especially for roles involving user-facing workflows or platform experiences.

Responsibilities

Define and drive the technical vision for AI-native Service Operations capabilities that support modern, proactive, and increasingly autonomous enterprise operations.
Architect scalable, reliable, and extensible systems that power service delivery, operational intelligence, automation, data processing, and AI-assisted workflows.
Lead the design of core frameworks and platform capabilities that enable teams to build consistent, intelligent, and highly reliable Service Operations products.
Partner with product, design, platform, and engineering leaders to translate Service Operations strategy into executable technical roadmaps.
Drive large-scale technical initiatives from concept through delivery, balancing innovation, customer value, scalability, reliability, security, and long-term maintainability.
Establish architecture patterns, engineering standards, and best practices for distributed systems, microservices, observability, performance, data quality, and production readiness.
Build and evolve systems that support proactive, predictive, AI-assisted, and agentic workflows across enterprise environments.
Help shape how AI agents, automation, and human workflows come together to improve service reliability, reduce manual effort, and increase productivity.
Ensure Service Operations capabilities remain industry-leading by continuously evaluating technology trends, customer needs, scalability demands, and platform evolution.
Design and develop reusable software components and frameworks that improve engineering velocity and product consistency across teams.
Guide teams through complex technical decisions, design reviews, code reviews, architecture tradeoffs, and execution planning.
Mentor senior engineers and technical leaders, helping raise the bar for engineering craftsmanship, system thinking, and operational excellence.
Foster a culture of continuous learning, innovation, quality, and accountability across the engineering organization.
Help teams adopt AI-powered tools and automation to improve engineering productivity, product reliability, and the speed of innovation.