Principal Machine Learning Engineer

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.

Join us to put AI to work for people.

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.

Emerging tech is a small senior group inside AI Engineering and Delivery. We turn early bets on AI and emerging tech into strategic capability for our customers, our people, and ServiceNow. We are working to unlock features that will be helping our platform and products evolve in line with the Fast paced world of Agentic AI — prioritizing robustness, performance, safety, and real-world customer impact at scale. You will design, build, and help build out production-grade agentic AI systems embedded across ServiceNow's platform — autonomous agents that reason over real enterprise data, take action across workflows, and stay safe at Fortune 500 scale.

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.

Technical leadership and strong bias for action. Set the architectural patterns the group works from. Own the hard design calls, run the design reviews, and raise the bar on agentic design and production AI discipline across engineers and principals.

Designing scalable and robust architectures that will support at scale deployment across hyperscalers and our own infrastructure.

Work on emerging model capabilities and applying them to real world customer problems on a short timeline

9+ years of software engineering with strong fundamentals in data structures, algorithms, and distributed systems.

Hands-on depth designing, shipping, and operating agentic systems in production — multi-agent orchestration, tool calling, planning loops, memory, and failure recovery. Not prototypes.

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.

Familiarity with RAG and retrieval patterns in production — vector stores, hybrid search, and retrieval evaluation metrics.

Track record of technical leadership: architecture ownership, code quality bar-raising, and mentoring engineers on production AI practices.

Nice to Have

Deeper specialization in search and retrieval at scale 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

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 multiple problem spaces at the frontier of what we do.

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 $240,100 - $420,200 , 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.

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.

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.

We strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact globaltalentss@servicenow.com for assistance.

For positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.

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