Principal Machine Learning Engineer

Company Description

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, Service Now is the AI control tower for business reinvention. Our Service Now 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 Deliveryis 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.

About the Team

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 Service Now. 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  will design, build, and help build out production-grade agentic AI systems embedded across Service Now's platform — autonomous agents that reason over real enterprise data,take actionacross workflows, and stay safeat Fortune 500 scale.

Your core focus areas:

• Agentic architecture.
  
  Design and ship multi-agent systems — orchestration, tool use, planning loops, memory, and failure recovery — thatoperatereliably in production, not in notebooks.
• Enterprise-grounded reasoning.
  
  Build agents that leverage Service Now'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 inaccurate, low-latency retrieval — RAG pipelines, hybrid search, re-ranking, and evaluation — as a critical dependencyofagentic 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

To be successful in this role you have:

• 9+ years of software engineering with strong fundamentals in data structures, algorithms, and distributed systems.
• Hands-on depth designing, shipping, andoperatingagentic systems in production — multi-agent orchestration, tool calling, planning loops, memory, and failure recovery. Not prototypes.
• Production-grade Python.
  
  Systemslanguage (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 recordof 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 orMLOps/model observability.
• Published work…