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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Staff Machine Learning Engineer - **Company:** ServiceNow - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $201,300.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Code Review, Cyber Security, Computer Programming, Databases, Cursor (Graphical User Interface Elements), Distributed Systems, Identity and Access Management, Python (Programming Language), Machine Learning, Software Safety, Search Technologies, Software Engineering, TypeScript, Software Vulnerability Management, Large Language Models, Model Validation, Cyber Threat Analysis, Containerization, Information Technology, Servicenow - **Published:** September 12, 2026 - **Apply:** https://jobs.smartrecruiters.com/ServiceNow/744000149124269-senior-staff-machine-learning-engineer ## About the Role * A track record of owning architecture across a large system or multiple teams, with deep experience operating production-quality software. * Hands-on depth in both agentic and LLM systems and probabilistic or ML-driven scoring-graph modeling, calibration, search and optimization, or risk and probability engineering. * Proven zero-to-one at scale: you've taken an ambiguous problem to a reliable production system that others depend on. * The judgment to make consequential architecture decisions under uncertainty, and make them defensible to engineers and executives alike. * Command of distributed systems, APIs, cloud-native development, and data or graph systems. * Expert-level Python, and/or Java, Go, or TypeScript. * Technical leadership and mentorship that moves teams through influence. * Applied interest in security problems-attack-path analysis, vulnerability management, identity security, threat intelligence, or detection and response-is strongly preferred. * Experience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf is a plus. * Experience with AI evaluation, safety, governance, or policy guardrails is a plus., * 10+ years of software engineering experience, including leading the design and delivery of complex production systems. * Demonstrated experience as the technical owner or lead for a major system or across teams. * Depth building AI/ML-powered production systems; probabilistic modeling, graph analytics, or calibration and evaluation is a strong plus. * Modern AI experience: LLMs, RAG, embeddings, vector search, agentic harness and workflows, model evaluation, or AI observability. * Strong programming experience in Python and/or Java, Go, or a similar language. * Cloud-native technologies, distributed systems, APIs, databases, and scalable architectures. * Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience. * Cybersecurity or security-product experience, or familiarity with modern security architectures and operations, is strongly preferred. ## Description As a Senior Staff Engineer, you own the architecture of an security harness with novel exploitability engine end to end, and you're accountable for the decisions that shape everything downstream. You set technical direction, make the hard calls defensible, and multiply the engineers around you. What you'll own * The end-to-end architecture of the exploitability engine-from evidence ingestion and entity resolution, through the attack-path probability core and choke-point ranking, to the validation loop that keeps predictions honest. * The decisions that cascade through the system: calibrated probability versus ordinal rank, identity as a first-class graph edge, assume-breach seeding, and how the most critical assets are defined. These are model-shaping calls, not implementation details. * The probabilistic ranking core: edge-traversal probability, guided path search with hop and likelihood limits, correlated-control-failure modeling, and honest uncertainty bands. * The calibration and validation loop-canaries, purple-team and incident replay, calibration measured by zone and vector-that turns modeled weights into evidence rather than opinion. * Make-or-break metrics as first-class engineering targets, starting with entity-resolution accuracy and calibration quality. * The build-on strategy-extending the existing portfolio rather than rebuilding it, and knowing precisely what to reuse and what must be net-new. What you'll do * Lead zero-to-one work at production scale: turn an ambiguous, novel problem into a reliable system other teams build on, and set the bar where no precedent exists. * Drive technical direction across architecture, design, and code reviews, and raise the engineering bar across the incubation. * Mentor senior engineers and lead by influence, not title. * Partner with product, security R&D, SecOps to turn customer problems into architecture, and translate that architecture into decisions leaders can act on. * Establish AI safety, security, governance, and guardrails for agentic systems running in production., 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. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Do TypeScript without TypeScript](https://www.wearedevelopers.com/videos/327-do-typescript-without-typescript) - [Applying Agile Principles to Incident Management ](https://www.wearedevelopers.com/videos/101-applying-agile-principles-to-incident-management) - [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases) - [AI in Production: applied AI & enterprise use cases](https://www.wearedevelopers.com/videos/100130-ai-in-production-applied-ai-enterprise-use-cases) - [Vuejs and TypeScript- Working Together like Peanut Butter and Jelly](https://www.wearedevelopers.com/videos/127-vuejs-and-typescript-working-together-like-peanut-butter-and-jelly) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)