{"id":2969088,"url":"https://alion.io/job/servicenow-senior-staff-machine-learning-engineer-9","title":"Senior Staff Machine Learning Engineer","company":{"id":97,"name":"ServiceNow","domain":"servicenow.com","url":"https://alion.io/company/servicenow","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":80,"open_postings":38,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-10-23T05:54:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Santa Clara, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":201300,"max":352300,"currency":"USD","period":"year","gross":null,"usd_annual":352300},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Claude Code","optional":false},{"name":"Cursor","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Python","optional":false},{"name":"ServiceNow","optional":false},{"name":"TypeScript","optional":false},{"name":"Windsurf","optional":false},{"name":"Anomaly Detection","optional":true},{"name":"Clean Architecture","optional":true},{"name":"Embeddings","optional":true},{"name":"Machine Learning","optional":true},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-10-20T00:50:44Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-23T15:39:55Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"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, ServiceNow is the AI control tower for business reinvention. Our ServiceNow 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.\nJoin us to put AI to work for people.\nAbout the team\nThe Security and Risk Engineering organization builds scalable, AI-powered security solutions that reduce risk and protect ServiceNow and its customers. We value AI-first thinking, clean architecture, intuitive experiences, and a culture of continuous learning.\nThis is a zero-to-one incubation. We’re building a new class of exposure analysis that ranks security work by exploitability-where an attacker could realistically get in-rather than raw severity. The architecture is evolving, and this role helps define what good looks like.\nThe role\nAs 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.\nWhat you’ll own\nThe 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. \nThe 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. \nThe probabilistic ranking core: edge-traversal probability, guided path search with hop and likelihood limits, correlated-control-failure modeling, and honest uncertainty bands. \nThe 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. \nMake-or-break metrics as first-class engineering targets, starting with entity-resolution accuracy and calibration quality. \nThe build-on strategy-extending the existing portfolio rather than rebuilding it, and knowing precisely what to reuse and what must be net-new. \nWhat you’ll do\nLead 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. \nDrive technical direction across architecture, design, and code reviews, and raise the engineering bar across the incubation. \nMentor senior engineers and lead by influence, not title. \nPartner with product, security R&D, SecOps to turn customer problems into architecture, and translate that architecture into decisions leaders can act on. \nEstablish AI safety, security, governance, and guardrails for agentic systems running in production. \nWhat you bring\nA track record of owning architecture across a large system or multiple teams, with deep experience operating production-quality software. \nHands-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. \nProven zero-to-one at scale: you’ve taken an ambiguous problem to a reliable production system that others depend on. \nThe judgment to make consequential architecture decisions under uncertainty, and make them defensible to engineers and executives alike. \nCommand of distributed systems, APIs, cloud-native development, and data or graph systems. \nExpert-level Python, and/or Java, Go, or TypeScript. \nTechnical leadership and mentorship that moves teams through influence. \nApplied interest in security problems-attack-path analysis, vulnerability management, identity security, threat intelligence, or detection and response-is strongly preferred. \nExperience with AI-assisted development tools and coding agents such as Claude Code, Codex, Cursor, or Windsurf is a plus. \nExperience with AI evaluation, safety, governance, or policy guardrails is a plus. \n 10+ years of software engineering experience, including leading the design and delivery of complex production systems. \nThe primary skills required are Predictive Modeling, Graph ML, Security Analytics, Anomaly Detection, Risk Scoring, LLMs/Agentic AI, and MLOps, which align with advanced machine learning and AI engineering rather than traditional software development.\nDemonstrated experience as the technical owner or lead for a major system or across teams. \nDepth building AI/ML-powered production systems; probabilistic modeling, graph analytics, or calibration and evaluation is a strong plus. \nModern AI experience: LLMs, RAG, embeddings, vector search, agentic harness and workflows, model evaluation, or AI observability. \nStrong programming experience in Python and/or Java, Go, or a similar language. \nCloud-native technologies, distributed systems, APIs, databases, and scalable architectures. \nBachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline, or equivalent practical experience. \nCybersecurity or security-product experience, or familiarity with modern security architectures and operations, is strongly preferred. \nFor positions in this location, we offer a base pay of $201,300 - $352,300, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies, and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.\n Work Personas\nWe 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.\nEqual Opportunity Employer\nServiceNow is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, age, disability, gender identity, veteran status, or any other category protected by law. In addition, all qualified applicants with arrest or conviction records will be considered for employment in accordance with legal requirements.\nAccommodations\nWe strive to create an accessible and inclusive experience for all candidates. If you require a reasonable accommodation to complete any part of the application process, or are unable to use this online application and need an alternative method to apply, please contact  for assistance.\nExport Control Regulations\nFor positions requiring access to controlled technology subject to export control regulations, including the U.S. Export Administration Regulations (EAR), ServiceNow may be required to obtain export control approval from government authorities for certain individuals. All employment is contingent upon ServiceNow obtaining any export license or other approval that may be required by relevant export control authorities.\nFrom Fortune. ©2026 Fortune Media IP Limited. All rights reserved. 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