Job description

What You Will Do • Set the technical vision for the Moveworks agentic AI harness, including planning, tool selection, context management, memory, critique, reflection, adaptation, and recovery. • Invent and prototype algorithms that help agents complete longer, more complex tasks with less supervision and stronger outcome reliability. • Design model-agnostic abstractions for combining foundation models, specialized agents, enterprise tools, policies, and deterministic workflows. • Develop approaches for agent memory and learning from execution traces, user feedback, and task outcomes while preserving enterprise security and privacy. • Create rigorous benchmarks and evaluation methods for tool-use correctness, plan quality, task completion, groundedness, policy compliance, latency, and cost. • Explore methods such as iterative planning, ReAct-style execution, self-critique, reflection, test-time computation, multi-agent coordination, and verifier-guided reasoning. • Build high-fidelity prototypes, identify the ideas that merit investment, and work with platform and product teams to define a practical path to production. • Partner across Agentic Systems, Search, Relevance, ML Infrastructure, Product, and Security to connect agent intelligence with enterprise knowledge, permissions, and actions. • Remain hands-on with code and experiments while influencing architecture, mentoring senior engineers, and raising the technical bar across the organization. What You Bring • Deep expertise in machine learning or artificial intelligence, paired with strong systems judgment and the ability to reason about end-to-end product architecture. • A record of meaningful work on AI agents, reasoning and planning, tool use, coding agents, conversational systems, reinforcement learning, or closely related areas. • Experience turning ambiguous research questions into working prototypes, measurable hypotheses, and durable technical direction. • Strong understanding of the tradeoffs among model capability, context, memory, latency, cost, reliability, and system complexity. • Experience designing evaluations for probabilistic systems, including benchmarks that measure task outcomes rather than surface-level response quality alone. • Excellent programming skills and the ability to work directly in modern ML and agent stacks. Python expertise is expected; experience with production systems languages is valuable. • Evidence of technical leadership across teams, including the ability to create clarity, influence roadmaps, and guide other senior engineers without relying on formal authority. • Typically 8 or more years of relevant industry or research experience, or an equivalent record of exceptional technical and research impact. Preferred Experience • A master's degree or PhD in computer science, machine learning, artificial intelligence, or a related field, or equivalent practical experience. • Published research, influential open-source work, or widely adopted systems in agents, language models, reasoning, evaluation, or human-agent interaction. • Experience developing new agent architectures or core AI products in a research-intensive organization. • Experience with enterprise requirements such as identity, authorization, auditability, privacy, policy enforcement, and safe tool execution. • Experience moving ideas from prototype to production with distributed systems and infrastructure teams. What Success Looks Like In your first year, you will establish the architecture and research roadmap for the agentic AI harness, deliver prototypes that improve performance on real enterprise tasks, create evaluations that make those gains measurable, and guide successful approaches into the Moveworks Reasoning Engine and broader AI platform. For 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. Work Personas 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. Equal Opportunity Employer ServiceNow 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.  Accommodations We 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 globaltalentss@servicenow.com for assistance. Export Control Regulations For 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. From Fortune. ©2026 Fortune Media IP Limited. All rights reserved. Used under license.

Requirements

Must-haves

Set the technical vision for the Moveworks agentic AI harness, including planning, tool selection, context management, memory, critique, reflection, adaptation, and recovery.
Invent and prototype algorithms that help agents complete longer, more complex tasks with less supervision and stronger outcome reliability.
Design model-agnostic abstractions for combining foundation models, specialized agents, enterprise tools, policies, and deterministic workflows.
Develop approaches for agent memory and learning from execution traces, user feedback, and task outcomes while preserving enterprise security and privacy.
Create rigorous benchmarks and evaluation methods for tool-use correctness, plan quality, task completion, groundedness, policy compliance, latency, and cost.
Explore methods such as iterative planning, ReAct-style execution, self-critique, reflection, test-time computation, multi-agent coordination, and verifier-guided reasoning.
Build high-fidelity prototypes, identify the ideas that merit investment, and work with platform and product teams to define a practical path to production.
Partner across Agentic Systems, Search, Relevance, ML Infrastructure, Product, and Security to connect agent intelligence with enterprise knowledge, permissions, and actions.
Remain hands-on with code and experiments while influencing architecture, mentoring senior engineers, and raising the technical bar across the organization.
Deep expertise in machine learning or artificial intelligence, paired with strong systems judgment and the ability to reason about end-to-end product architecture.
A record of meaningful work on AI agents, reasoning and planning, tool use, coding agents, conversational systems, reinforcement learning, or closely related areas.
Experience turning ambiguous research questions into working prototypes, measurable hypotheses, and durable technical direction.
Strong understanding of the tradeoffs among model capability, context, memory, latency, cost, reliability, and system complexity.
Experience designing evaluations for probabilistic systems, including benchmarks that measure task outcomes rather than surface-level response quality alone.
Excellent programming skills and the ability to work directly in modern ML and agent stacks. Python expertise is expected; experience with production systems languages is valuable.
Evidence of technical leadership across teams, including the ability to create clarity, influence roadmaps, and guide other senior engineers without relying on formal authority.

Nice-to-haves

A master's degree or PhD in computer science, machine learning, artificial intelligence, or a related field, or equivalent practical experience.
Published research, influential open-source work, or widely adopted systems in agents, language models, reasoning, evaluation, or human-agent interaction.
Experience developing new agent architectures or core AI products in a research-intensive organization.
Experience with enterprise requirements such as identity, authorization, auditability, privacy, policy enforcement, and safe tool execution.
Experience moving ideas from prototype to production with distributed systems and infrastructure teams.
What Success Looks Like
Compensation is based on the geographic location in which the role is located and is subject to change based on work location.
Work Personas

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aireactmlmachine learningpython

Platform: smartrecruiters
Effort: medium

Benefits

Benefits detected in the listing.

Vision

401(K)