Senior Machine Learning Engineer

The Machine Learning Developer designs, builds, ships, and operates applications whose core behavior is model-driven rather than explicitly authored. The Developer builds the engine behind ServiceNow's optimization products — the algorithms and pipelines that turn sophisticated scheduling and resource-allocation problems into fast, reliable customer outcomes. The work is evolving — where "AI-driven" once meant applying ML techniques to optimization problems, it now also means using AI agents as part of the build process itself. This role expects both: someone who can design an efficient algorithm from scratch, and someone who can specify a problem precisely enough to direct an AI agent to implement part of it, then verify the result is correct. The developer owns the correctness, performance, and robustness of what ships, regardless of whether a human or an agent wrote it.

What you get to do:

• Build Optimization applications. Design, build and operate applications built around advanced optimization models in production at scale. This includes API design, data ingestion, multiprocessing, observability, advanced algorithm development, performance benchmarking, and automated testing.
• Build automated evaluation and test model behaviour. Design and operate the evaluation system that supports development and delivering features with confidence.
• Specify precisely and direct AI coding agents. Convert requirements into testable specifications with explicit scope, constraints, and acceptance criteria; decompose work into agent-sized tasks; and review agent output for correctness and maintainability. Contribute to teams AI-harness helping shape the way agents are leveraged.
• Own quality, safety, and reliability in production. Manage deployments, monitor service availability, implement alerting and respond to customer issues before they even know they have a problem.
• Deliver as a forward deployed engineer. Work closely with customers when the work calls for it. Understanding business requirements and product capabilities to help customers get to value faster and use learnings to influence product roadmap.
• Collaborate across product, design, and engineering. Partner with product managers, designers, conversation designers, and engineers to define success criteria, align on tradeoffs, and communicate capability and risk clearly.