{"id":2741513,"url":"https://alion.io/job/servicenow-staff-machine-learning-engineer-9","title":"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":33,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":18,"computed_at":"2026-10-17T05:46:00Z"}},"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":176100,"max":308200,"currency":"USD","period":"year","gross":null,"usd_annual":308200},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Kubeflow","optional":false},{"name":"LLM","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"ServiceNow","optional":false},{"name":"SQL","optional":false},{"name":"Vertex AI","optional":false},{"name":"A2A","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Model Context Protocol","optional":true}],"status":"live","first_seen_at":"2026-10-16T20:37:07Z","employer_posted_date":"2026-10-16","last_verified_at":"2026-10-17T21:46:19Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"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 Agentic Engineering organization at ServiceNow is the customer-obsessed engineering group that builds a conversational AI experience that turns enterprise intent into completed work. We advance how enterprise AI reasons, remembers, and executes.\nThe Agent Orchestration team - the team you'll join - owns the execution core: the agent harness, orchestration runtime, multi-agent coordination, memory management, and the evaluation frameworks that ensure agents behave correctly in production. Every autonomous action Otto promises depends on what this team ships.\nBy joining our team, you’ll be at the forefront of our AI transformation journey, backed by the global scale of ServiceNow and the agility of a high-growth environment. We are looking for world-class talent to help us extend agentic AI to every employee across every corner of the business\nWhat you get to do in this role:\nAs a Staff AI Engineer, you will own significant parts of the agent harness - the infrastructure layer that enables AI agents to reason over real enterprise data, take action across workflows, and run safely at Fortune 500 scale.\nHarness engineering: Design and build the agent execution harness - the orchestration layer that routes inputs, manages context, invokes tools, handles retries, and surfaces execution state across multi-step agentic workflows\nReliability at scale: Own the runtime's fault tolerance, latency, and throughput; design for enterprise workflows that cannot fail silently or unpredictably\nObservability: Instrument the harness with tracing, cost attribution, and latency visibility so the team can reason about agent behavior in production and catch failures before customers do\nPrompt infrastructure: Build prompt management systems - versioning, templating, and systematic evaluation - that keep agent behavior stable across model updates and configuration changes\n Eval engineering: Design and own evaluation frameworks (unit evals, integration evals, production monitors) that measure agent quality, catch regressions, and drive data-informed decisions\nLLM integration: Integrate with and abstract over frontier LLMs, managing model routing, fallback strategies, cost, and latency trade - offs in production\nTechnical leadership: Set technical standards through architecture decisions, code reviews, and coaching - particularly on agentic design patterns and production AI discipline.\nSystem boundary design: Define where agent logic lives - what's a tool call, a sub-agent, a hardcoded path, or a human escalation - and establish those design standards across the team\n To be successful in this role you have:\n7+ years building production software systems with a strong track record on reliability, performance, and scalability\nHands-on experience shipping generative AI products - not just integrating LLM APIs or building prototypes, but owning AI-powered features that production users depend on\nSolid depth in how large language models work: failure modes, context constraints, and how prompt design shapes model behavior at scale\nPython, Kubeflow,Experience with SageMaker, Vertex AI or AzureML, SQL\nPractical prompt engineering experience: systematically designing, versioning, and evaluating prompts across model updates or A/B evaluation cycles\nA real track record in eval engineering - not just familiarity, but a portfolio of evaluation suites designed, shipped, and used to drive quality decisions in production AI systems\nCost and efficiency awareness at the system level: experience reasoning about model routing, inference cost, and latency tradeoffs in production\nStrong software engineering fundamentals: distributed systems, API design, and testing discipline\nComfort operating in fast-moving, ambiguous, startup-like AI product environments\nNice to Have\nExperience with multi-agent coordination patterns (A2A, MCP)\nFamiliarity with agent frameworks (LangChain, LlamaIndex, or similar)\nPrior experience shipping AI systems in enterprise software\nExperience with AI observability tooling (tracing, cost tracking, LLM-specific monitoring)\nFamiliarity with cloud-native infrastructure, service observability, logging, monitoring, reliability engineering, and production troubleshooting\nFor positions in this location, we offer a base pay of $176,100 - $308,200, 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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