Senior Staff Applications Development Engineer

Job description

We are the Industry Vertical Engineering team at ServiceNow. We design and build specialized software solutions tailored to the unique workflows, data models, and regulatory compliance needs of specific sectors, with a particular focus on high-tech and adjacent regulated industries such as telecommunications, healthcare, financial services, and manufacturing, rather than relying on generic, one-size-fits-all solutions. We are building the next generation of industry-specific enterprise applications, and increasingly these applications are AI-native: their core behavior is model-driven rather than explicitly authored. They interpret user intent, reason over context, invoke tools, and act on the user's behalf, all while respecting the complex business workflows, specialized data models, and evolving regulatory requirements of the industries we serve. As a Senior Staff Software Engineer, you will design, build, ship, and operate AI-native industry applications whose most important logic often lives in natural language (instructions, prompts, tool descriptions, and guardrails) as much as in code. You will work across the full software development lifecycle, and often embedded directly with customers as a forward deployed engineer, building against their data, integrations, and channels. Alongside hands-on delivery, you will drive engineering excellence, mentor other engineers, and influence architecture and long-term platform direction across multiple teams. What you get to do in this role: • Design, build, and deliver scalable, AI-native enterprise applications for industry-specific workflows, data models, and compliance requirements, along with the integrations and channels that make them usable in production. • Build agentic behavior into those applications: intent interpretation, multi-step reasoning, tool and function invocation, and action on the user's behalf, delivered through conversational experiences across chat (and voice where the use case calls for it) that hold context and hand off cleanly between automated and human agents. • Design AI-driven autonomous workflows: decompose business processes into the steps and decision points an agent can execute, decide where autonomy is appropriate and where a human checkpoint is required, and define how exceptions, retries, and hand-back to a person are handled. • Author and maintain agentic instructions (system instructions, role definitions, tool descriptions, guardrails, and escalation rules) as versioned engineering artifacts under review and regression coverage, not configuration text. • Engineer prompts for reliability rather than demo quality, iterating against measured outcomes: task decomposition, golden examples, structured output schemas, grounding and citation, graceful failure, and token and latency cost. • Build automated evaluation and test non-deterministic behavior: golden datasets, multi-turn conversation suites, model-as-judge scoring calibrated to human review, CI gates, drift detection, and adversarial, jailbreak, grounding, and tool-selection testing. • Design software that lets customers configure and extend platform capabilities without sacrificing performance, reliability, or maintainability, and write clean, reusable, well-tested code following engineering best practices, including code reviews, unit testing, and test automation. • Specify precisely and direct AI coding agents: convert requirements into testable specifications with explicit scope, constraints, and acceptance criteria, decompose work into agent-sized tasks, and review agent output for correctness and maintainability. You own the result regardless of what produced it. • Deliver as a forward deployed engineer, embedded with customers when the work calls for it: building against their data, integrations, and channels, tuning instructions and evaluation sets in their environment, and returning with evidence that improves the product. • Own quality, safety, and reliability in production: monitor conversation quality, containment, hallucination, and unsafe actions, defend against prompt injection and data leakage, and feed production failures back into specifications and evaluation sets. Troubleshoot and optimize performance, scalability, and reliability across distributed systems. • Serve as a technical leader: mentor engineers, promote knowledge sharing, drive engineering best practices, and lead complex technical initiatives spanning multiple teams while influencing architecture and long-term platform direction. • Partner with product managers, designers, and stakeholders to translate complex business and regulatory requirements into scalable technical solutions, align on tradeoffs, and communicate capability, limitation, and risk clearly to non-engineers To be successful in this role you have: • Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry. • 10+ years of professional software engineering experience in the SaaS industry, building and operating products at production scale. • A demonstrated track record of building, shipping, and operating production software, including hands-on delivery of AI-native application features that real users depend on rather than demos. • Direct, hands-on experience authoring agentic instructions and prompts, designing AI-driven autonomous workflows, and building the evaluation and testing that verifies non-deterministic behavior. • Advanced command of data structures, algorithms, object-oriented design, design patterns, system design, APIs, and performance optimization. • Strong grounding in modern application architecture across cloud, distributed systems, and service-oriented or platform technologies, with solid data modeling and storage fundamentals. 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