Senior Staff Machine Learning Engineer, ServiceNow

• 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

Desirable

• 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
• 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
• 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