Staff Software Engineer

Job Description ServiceNow is looking for a Staff Software Engineer to help design, build, and operate the next generation of AI-powered platform services. This role is not limited to traditional machine learning engineering. We are looking for a strong software engineer and technical lead with deep software design and architecture skills, strong coding ability, and the technical judgment to build reliable production systems at enterprise scale. Historically, ServiceNow has been built around a large monolithic Java service known internally as Glide. In this team, we are modernizing the stack by building “off-Glide” Kubernetes-based microservices that operate separately from the standard Glide Java codebase. These services are primarily written in Python, with occasional integration points into Java and the broader ServiceNow platform. The ideal candidate has built and operated containerized backend services in Kubernetes environments, understands distributed systems and production reliability, and can lead technical design across ambiguous problem spaces. Prior experience working on AI, GenAI, ML-powered, or data-intensive products is a strong plus. About the Team This team is building modern AI platform services that extend ServiceNow’s core platform beyond the traditional monolithic architecture. We are focused on production-grade services that bring together enterprise data, AI model integrations, scalable APIs, evaluation workflows, and reliable deployment patterns. You will work on systems that must be secure, observable, performant, and maintainable while serving real customer workflows. The work spans backend service design, distributed architecture, AI/GenAI product integration, Kubernetes-based deployment, CI/CD, model evaluation, and technical leadership across engineering teams. Types of problems you’ll get to work on You will design, build, and operate production‑grade AI platform microservices that run outside the traditional Glide Java codebase while still integrating cleanly with the broader ServiceNow ecosystem. Your work may include: Building off-Glide Python microservices deployed on Kubernetes. Designing APIs, service boundaries, data models, and integration patterns for AI-enabled products. Modernizing monolithic workflows into scalable, independently deployable services. Building reliable distributed systems that handle concurrency, queueing, retries, fairness, backpressure, and failure recovery. Integrating frontier AI SDKs such as Anthropic, Google, and OpenAI into production software systems. Applying prompt engineering, structured outputs, model evaluation, and production observability to GenAI use cases. Designing evaluation and benchmarking workflows for AI‑powered product capabilities. Partnering with platform, product, security, and infrastructure teams to ship enterprise‑grade services. Raising the engineering bar through architecture reviews, code reviews, mentoring, and technical direction. Your core focus areas Off‑Glide microservices architecture Design and ship modern backend services that run independently from the core Glide Java monolith. Define clean service boundaries, APIs, deployment patterns, and operational practices for Kubernetes-based services. Production‑grade backend engineering Build reliable, maintainable, and scalable services using strong software engineering fundamentals. Write high-quality code, review critical changes, improve performance, and drive long‑term technical decisions. Kubernetes and containerized application delivery Develop, deploy, and operate containerized applications in Kubernetes or similar environments. Help define patterns for configuration, release, observability, rollback, and operational readiness. Distributed systems and data pipelines Design systems that handle large payloads, high concurrency, asynchronous processing, message queues, state management, cloud storage, and distributed coordination. Build abstractions that can support multiple infrastructure backends such as PostgreSQL, Redis, Kafka, object storage, or equivalent technologies. AI/GenAI product integration Work with AI and GenAI capabilities as part of production software systems. This may include frontier model SDKs, prompt engineering, structured outputs, agentic patterns, AI coding assistants, RAG‑style workflows, document/data processing, and model evaluation. Evaluation, quality, and production readiness Develop automated evaluation, benchmarking, testing, and observability workflows for AI‑powered capabilities. Evaluate trade‑offs across latency, quality, cost, reliability, and maintainability. Technical leadership Lead through technical judgment, architecture ownership, code quality, mentoring, and cross‑functional alignment. Help other engineers make sound design decisions and ship production‑quality systems. Qualifications To be successful in this role, you have: 8+ years of professional software engineering experience, with strong fundamentals in data structures, algorithms, distributed systems, APIs, and backend service design. Demonstrated experience as a technical lead, Staff Engineer, or equivalent senior individual contributor responsible for architecture, technical direction, code quality, and mentoring. Strong backend engineering skills with hands‑on experience designing, building, and operating production services. Strong Python engineering skills, or demonstrated ability to quickly become productive in Python for backend service development. Experience with Java or large enterprise Java codebases is a plus, especially for integration with existing platform services. Hands‑on experience building or operating containerized applications deployed in Kubernetes, OpenShift, or similar orchestration environments. Experience modernizing monolithic systems into microservices, distributed services, or independently deployable backend components. Experience designing scalable APIs, asynchronous processing systems, queues, event‑driven services, or data‑processing pipelines. Familiarity with cloud‑native infrastructure, service observability, logging, monitoring, reliability engineering, and production troubleshooting. Ability to make pragmatic architecture decisions across performance, reliability, security, maintainability, and delivery speed. Strong coding skills and the ability to raise the engineering bar through code reviews, design reviews, and technical mentorship. Strong communication skills and the ability to partner across product, engineering, infrastructure, security, and research‑oriented teams. Preferred Qualifications Strong candidates may also have: 10+ years of backend or distributed systems engineering experience. Prior experience working on AI, GenAI, ML-powered, NLP, document processing, search, knowledge extraction, or data‑intensive products. Experience building large‑scale document ingestion, document conversion, data transformation, or AI data pipeline systems. Experience with production AI quality workflows, including model evaluation, benchmarking, golden datasets, regression testing, or automated release evaluation. Working experience with frontier AI SDKs such as Anthropic, Google, or OpenAI. Familiarity with prompt engineering, structured outputs, tool calling, agentic design patterns, Model Context Protocol, or AI‑assisted development workflows. Knowledge of MLOps or applying machine learning models to production use cases. Experience with FastAPI or similar Python service frameworks. Experience with infrastructure backends such as PostgreSQL, Redis, Kafka, RabbitMQ, S3‑compatible object storage, or equivalent technologies. Experience with CI/CD, automated deployment workflows, release management, DevOps, or operating services outside a centralized platform release process. Experience with observability tools such as Prometheus, Grafana, Instana, or equivalent monitoring/logging platforms. Experience with security ownership, vulnerability management, CVE triage, secure development practices, or enterprise compliance expectations. Experience contributing to open‑source or inner‑source platforms, reusable frameworks, or shared infrastructure components. Published work, patents, conference papers, or open‑source contributions related to AI systems, knowledge systems, search, retrieval, or large‑scale data processing. Experience collaborating with research teams or translating advanced AI/ML capabilities into production software. Experience mentoring engineers and helping them grow through design feedback, code review, onboarding, and technical coaching. Compensation and Benefits For positions in this location, we offer a base pay of $166,500 - $291,400 , plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location. Equal Opportunity Employer ServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements. #J-18808-Ljbffr ServiceNow