Senior Staff Machine Learning Engineer - Agentic AI

We approach our distributed world of work with flexibility and trust. Work personas (flexible, remote, or required in office) are categories that are assigned to ServiceNow employees depending on the nature of their work and their assigned work location. Learn more here. To determine eligibility for a work persona, ServiceNow may confirm the distance between your primary residence and the closest ServiceNow office using a third-party service.

To be successful in this role you have:

• 8+ years of software engineering with strong fundamentals in data structures, algorithms, and distributed systems.
• Formal grounding in machine learning fundamentals - modeling, training, evaluation, and the principles behind modern deep learning, LLMs, and agent architectures.
• Hands-on depth designing, shipping, and operating agentic systems in production - multi-agent orchestration, tool calling, planning loops, memory, and failure recovery. Not prototypes.
• Proven experience building and operating production-grade, full-stack AI systems and services end to end - model integration, APIs, serving infrastructure, and the application layer.
• Production-grade Python. Systems language (Go, Java, or C++) is a plus.
• Working experience with frontier AI SDKs (Anthropic, Google, or OpenAI) - prompt engineering, structured outputs, and model evaluation in production settings.
• Track record of technical leadership: architecture ownership, code quality bar-raising, and mentoring engineers on production AI practices.

• Specialization in search and retrieval at scale - RAG pipelines, hybrid search, vector stores, re-ranking, and retrieval evaluation - or MLOps/model observability.
• Published work or open-source contributions in agentic systems or retrieval.
• Exposure to LLM fine-tuning or inference optimization in production.

Why join us

Intelligence is commoditizing. Context and execution are not. With 100B+ workflows, 6.5T transactions a year, and 85% of the Fortune 500 on our platform, we are building the system that makes AI actually work inside the enterprise - Sense, Decide, Act, Govern.

What’s shipping as we speak: AI Specialists autonomously resolving cases across IT, CRM, HR, and Security. Action Fabric opening our full system of action to any external agent via MCP - Anthropic’s Claude Cowork is the first design partner. Project Arc with NVIDIA bringing governed autonomous desktop agents into production. Build Agent live inside Cursor, Claude Code, and GitHub Copilot. AI Control Tower with kill-switch capabilities and cross-vendor agent governance. These are production systems at Fortune 500 scale, not roadmap slides.

You’d work across three problem spaces at the frontier of what we do:

Autonomous Enterprise: Self-driving business processes grounded in CMDB, Workflow Data Fabric, and Knowledge Graph - context no frontier lab can replicate,

Omni-channel AI Resolution: Production voice, chat, and computer-use agents with generative UI, live with customers today,

AI Control Tower: Identity, entitlements, and audit-grade compliance for every agentic system in the enterprise - nobody else has this layer.

You will build the substrate that connects all four: SENSE (any data) * DECIDE (any AI model) * ACT (any workflow) * GOVERN (identity + governance). The architectural inflection point is now.

For positions in this location, we offer a base pay of $201,300 - $352,300, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) 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.

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone-freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow- helping 85% of the Fortune 500® work smarter, faster, and better. We’re building an AI-native culture where technology and talent are unstoppable together. And we’re just getting started., AI Engineering and Delivery is the customer-obsessed engineering group building the agentic AI and enterprise-scale search systems that power Now Assist, AI Agents, and the AI-driven experiences our customers rely on every day. We build AI as foundational platform infrastructure - prioritizing robustness, performance, safety, and real-world customer impact at scale.

Types of problems you’ll get to work on

You will design, build, and operate production-grade agentic AI systems embedded across ServiceNow’s platform - autonomous agents that reason over real enterprise data, take action across workflows, and run safely at Fortune 500 scale.

Your core focus areas:

• Agentic architecture. Design and ship multi-agent systems - orchestration, tool use, planning loops, memory, and failure recovery - that operate reliably in production, not in notebooks.
• Enterprise-grounded reasoning. Build agents that leverage ServiceNow’s data layer - CMDB, Workflow Data Fabric, and Knowledge Graph - to make decisions with context no frontier model has on its own.
• Trust, safety, and governance. Own the guardrails: observability, human-in-the-loop controls, and compliance infrastructure that make autonomous systems safe to deploy at scale.
• Retrieval and grounding. Work closely with our search team to ensure agents are grounded in accurate, low-latency retrieval - RAG pipelines, hybrid search, re-ranking, and evaluation - as a critical dependency of agentic quality.
• Model integration and evaluation. Integrate frontier models (Anthropic, Google, OpenAI) into the Sense * Decide * Act * Govern architecture; evaluate trade-offs across cost, latency, and capability for production use cases.
• Engineering leadership. Raise the technical bar through architecture decisions, code reviews, and coaching - particularly on agentic design patterns and production AI discipline.