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ServiceNow

Builds software that automates business workflows

Staff Software Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeSanta Clara, CA, USA

About the Job

Requirements

  • Eight or more years of software development experience with a Bachelor's degree, six or more years with a Master's degree, three or more years with a PhD, or equivalent work experience.
  • At least five years of practical software development experience with modern cloud-native and AI-native systems.
  • At least three years of hands-on experience with large language model prompt engineering, agent design, and retrieval systems such as retrieval-augmented generation, semantic search, and knowledge bases.
  • At least three years of experience integrating third-party artificial intelligence and machine-learning services and platform APIs, such as OpenAI, Anthropic, AWS Bedrock, and Google Vertex AI.
  • At least two years of hands-on experience developing applications and extensions on the ServiceNow platform.
  • Strong proficiency in JavaScript/GlideScript, Python, and Java, plus familiarity with ServiceNow scripting patterns and APIs.
  • Understanding of the ServiceNow data model, form and workflow design, and business process automation.
  • Understanding of microservices architecture, REST and streaming APIs, and modern backend frameworks such as Spring Boot and FastAPI.
  • Experience designing and optimizing high-performance data pipelines, embedding systems, and vector stores at scale.
  • Expertise in large language model orchestration frameworks such as LangChain, LlamaIndex, and Semantic Kernel, and vector databases such as Pinecone, Weaviate, and pgvector.
  • Experience with cloud computing platforms and managed AI services, including AWS, Google Cloud Platform, and Azure; hands-on infrastructure-as-code experience.
  • Proficiency with Kubernetes, containerization, continuous integration and continuous delivery pipelines, and automated testing frameworks.
  • Experience developing and debugging evaluation harnesses, benchmarks, and metrics for AI and machine-learning features.
  • Ability to troubleshoot and isolate root causes in complex, distributed, and probabilistic systems.
  • Ability to explain technical concepts to technical and non-technical audiences.
  • Experience with Agile and Scrum development methodologies and iterative development and continuous improvement.
  • Ability to engage with customers, understand their needs, and support issue resolution.
  • Track record of taking ownership of significant technical initiatives and delivering them successfully.
  • Demonstrated mentorship of junior engineers or technical leadership in previous roles.

Responsibilities

  • Lead the design and architecture of complex, production-critical AI-native systems, including large language model integrations, agentic workflows, retrieval pipelines, and inference services.
  • Own end-to-end delivery of medium-to-large features or systems, including specification, design, implementation, testing, deployment, and production monitoring.
  • Make architectural and technical trade-off decisions among latency, cost, scalability, reliability, and model quality.
  • Establish evaluation frameworks, testing strategies, and production monitoring for AI-driven features, and drive adoption of evaluation-driven development practices.
  • Design and optimize high-performance data pipelines, embedding systems, and vector stores handling millions of rows and complex retrieval scenarios.
  • Champion security best practices, including prompt-injection defense, data privacy, personally identifiable information handling, responsible AI guardrails, and regulatory compliance.
  • Participate in architectural reviews across teams and contribute to platform-level technical decisions.
  • Evaluate emerging AI and machine-learning frameworks, services, and techniques, and recommend adoption where applicable.
  • Mentor junior and mid-level engineers through pair programming, design reviews, architecture discussions, and hands-on coaching.
  • Conduct code reviews, including evaluation of prompts, agent logic, retrieval strategies, and AI/model behavior validation.
  • Identify technical debt and improvement opportunities, and lead initiatives to address them.
  • Share knowledge through documentation, brown-bag sessions, and architectural guidance.
  • Contribute to hiring and interview processes by providing technical assessment and feedback on engineering candidates.
  • Model best practices in software craftsmanship, testing discipline, and debugging methodology.
  • Support onboarding of new team members and knowledge transfer across projects.
  • Work with product management, domain specialists, and engineering teams to define requirements and technical specifications.
  • Translate technical concepts and AI trade-offs for non-technical stakeholders, including product, leadership, and customers.
  • Lead root cause analysis for customer-reported issues involving model behavior, hallucinations, retrieval quality, and distributed-system complexity.
  • Participate in design reviews, technical planning sessions, and retrospectives, contributing perspectives on technical feasibility and risk.
  • Communicate design decisions, implementation challenges, AI limitations, and production incidents.
  • Build and maintain relationships with adjacent teams and external partners where applicable.
  • Identify opportunities to improve development processes, tooling, and team efficiency.
  • Develop robust, maintainable, and well-tested AI-native software using Python, Java, and JavaScript as appropriate.
  • Build unit tests, integration tests, and AI evaluation harnesses for accuracy, regression, and hallucination detection.
  • Troubleshoot complex issues in distributed systems, probabilistic models, and cloud infrastructure, and isolate root causes.
  • Implement secure coding practices and responsible AI guardrails throughout the development lifecycle.
  • Manage production deployment, monitoring, and iteration of features, and respond to performance issues and model drift.
  • Work with cloud services, including AWS, Google Cloud Platform, and Azure; managed AI services, including Bedrock, Vertex AI, and Azure OpenAI; Kubernetes; and continuous integration and continuous delivery pipelines.
  • Contribute to code and architecture standards and ensure adherence to established best practices.
  • Design and develop custom applications, extensions, and integrations on the ServiceNow platform.
  • Develop workflows, business rules, and automation using ServiceNow scripting, JavaScript, GlideScript, and REST APIs.
  • Build data models, forms, and dashboards for ServiceNow applications, and optimize database queries for performance.
  • Integrate third-party AI and machine-learning services and custom APIs with ServiceNow platform services.
  • Contribute to and maintain ServiceNow plugin and application architecture, following platform best practices and coding standards.
  • Participate in ServiceNow upgrades and configuration management, ensuring compatibility with platform updates.
  • Mentor junior engineers on ServiceNow platform capabilities and development patterns.
  • Participate in customer issue triage and root cause analysis for production ServiceNow application issues.
  • Work with customer success and support teams to understand and resolve complex technical issues.
  • Provide technical guidance to customers on application usage, configuration, and troubleshooting.
  • Identify patterns in customer issues and drive improvements to prevent recurrence.
  • Contribute to customer documentation and internal knowledge bases.
  • Support customer escalations and provide timely resolutions to critical issues affecting customer operations.
  • Gather customer feedback and communicate product improvement opportunities to product management.

Desired Qualifications

  • Customer-facing experience is a plus.

About the Company

ServiceNow

Builds software that automates business workflows

ServiceNow builds cloud software that helps large organizations manage work across IT, customer service, human resources, and security. Its applications handle tasks such as resolving support tickets, onboarding employees, and responding to customer requests. Businesses configure forms, approval steps, and automation through visual tools on a common platform, connecting existing systems and using AI to assist workers or carry out tasks. The company sells subscriptions to its applications and platform, with professional services helping customers implement and use the software. Founded in 2004, ServiceNow is headquartered in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
2004

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