Senior Staff Machine Learning Engineer - Agentic AI

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.

About the Team

AIEngineeringand Delivery is the customer-obsessedengineering 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 problemsyou’llget to work on

You will design, build, andoperateproduction-grade agentic AI systems embedded across Service Now's platform — autonomous agents that reason over real enterprise data,take actionacross 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 — 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.
• Engineering leadership.Raise the technical bar through architecture decisions, code reviews, and coaching — particularly on agentic design patterns andproductionAI discipline.

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.

Nice to Have

• 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 —…