Jobs in the United States

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Data Center Architect
Radiant
San Francisco, CA
We design and operate AI-native cloud platforms engineered for sovereignty, performance, and scale. Our infrastructure powers GPU-native workloads, multi-tenant control planes, and…
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Lead Platform Infrastructure Product Engineer
Radiant
San Francisco, CA
We design and operate AI-native infrastructure platforms engineered for sovereignty, performance, and scale - powering GPU-native workloads, multi-tenant control planes, and…
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Lead Cloud Product Engineer
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San Francisco, CA
We design and operate AI-native infrastructure platforms engineered for sovereignty, performance, and scale - powering GPU-native workloads, multi-tenant control planes, and…
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Lead ML Product Engineer
Radiant
San Francisco, CA
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Senior Field Product Engineer
Radiant
San Francisco, CA
We design and operate AI-native infrastructure platforms engineered for sovereignty, performance, and scale - powering GPU-native workloads, multi-tenant control planes, and…
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Infrastructure Tooling & Observability Engineer (UK)
Radiant
San Francisco, CA
…and at speed. As an Infrastructure Tooling & Observability Engineer, you will design and develop the systems that power visibility, automation, and operational intelligence across complex…
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HPC Infrastructure Site Reliability Engineer
Radiant
San Francisco, CA
We are looking for a senior Infrastructure Site Reliability Engineer with deep experience operating large-scale distributed systems and recent hands-on expertise in high-performance…
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Senior Manager, Social Media
Radiant
San Francisco, CA
…in AI infrastructure a strong plus. You do not need to be an engineer, but you need to be able to sit with technical leaders, understand what matters, find the angle, and translate it into…
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Manager, Development
Veradigm LLC
Remote, United States
Lead AI-first development. Make AI a core part of how the team builds software. Put AI coding agents to work across design, coding, code review, testing, and documentation. Set the…
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WI
(USA) Senior, Software Engineer
Walmart Inc.
United States
- $90,000 to $180,000 USD / year
Role summary: Walmart is seeking a Senior Software Engineer to lead the delivery of scalable, secure software solutions aligned with platform and business objectives. This role involves…
Added 1 min ago
WI
Staff, Software Engineer - FE IOS
Walmart Inc.
United States
- $143,000 to $286,000 USD / year
…collaborate with product teams, designers, and architects to design, develop, and enhance reliable and easy-to maintain mobile…
Added 1 min ago
AT
Autonomy Engineer
Apex Technology, Inc.
Los Angeles, CA
- $110,000 to $160,000 USD / year
…does not scale to a constellation of vehicles. As an Autonomy Engineer, you will turn manual operations procedures into automated, testable software, own fault protection across every bus,…
Added 1 min ago
Design Engineer - New Product Development
Fortifi Food Processing Solutions
Birmingham, OH
Design, develop, and test new products using disciplined engineering methods within the context of the Stage Gate process.
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Network Tech
Jarrell Independent School District
Jarrell, TX
Responsible for the design, implementation, monitoring, and maintenance of the organization's network systems. This position is responsible for supporting the operation of advanced…
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Structural Field Engineer
Bechtel
New Albany, OH
Bechtel is building a semiconductor manufacturing plant in New Albany, Ohio. Bechtel will design and build Phase 1 of the Ohio One project, a total of 2.5 million square feet, including…
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Process Engineer
Ecolab Inc.
Plano, TX
The Process Engineer is a fully competent, autonomous contributor responsible for executing and owning complex process design activities for water, wastewater, and ultrapure water (UPW)…
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Enterprise Crypto Security Engineer
KBR
El Segundo, CA · Colorado Springs, CO
Oversee the technical design, interface definition, development, and delivery of needed encryption units, including hardware and firmware. Ensure compatibility with subsystems and…
Added 3 mins ago
Senior Associate - Database Engineer
New York Life Insurance Co
United States
…approved relational or object database technologies. You will engineer, operate, harden, automate, and modernize database services supporting critical business workloads across private…
Added 3 mins ago
Senior Associate - Middleware Engineer
New York Life Insurance Co
United States
Serve as a senior engineering owner for New York Life's enterprise middleware platforms. You will engineer, operate, harden, and modernize the runtime and integration services that support…
Added 3 mins ago
RH
ServiceNow Platform Engineer
Robert Half
San Antonio, TX
- $90,000 to $110,000 USD / year
We are looking for an experienced ServiceNow Platform Engineer to support and strengthen enterprise platform operations in Texas. This role focuses on improving stability, scalability, and…
Added 4 mins ago
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Field Deployment Engineer
Astound
Seattle, WA
- $28 to $35 USD / hour
The Field Deployment Engineer is responsible for the installation, maintenance, troubleshooting, and administration of internet access infrastructure across company and customer…
Added 4 mins ago
Senior Product Development Engineer
USA 3M Company
MN
The Senior Product Development Engineer is versatile and supports Home Care’s Scotch-Brite™ portfolio in new product development, cost-out change-management programs (CMPs) and quality…
Added 5 mins ago
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Software Engineer, Fullstack, Spend Controls
Ramp
New York City, NY
- $172,000 to $240,000 USD / year
We’re looking for a fullstack engineer to build and improve these experiences, from the interfaces admins use to configure controls to the backend logic that makes them work reliably.…
Added 5 mins ago
Software Engineer, Consumer Subscriptions
SpaceXSI
Palo Alto, CA
As an engineer for Consumer Subscriptions (X Premium, SuperGrok, Cursor), you will play a key role in the entire billing and monetization infrastructure powering one of SpaceXSI's…
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IAM Engineer
Insight Global
Plano, TX
- From $62 USD / hour
Ability to design custom IAM integration solutions when out-of-the-box connectors are unavailable.

ServiceNow

Senior Machine Learning Engineer, Agent Eval Platform

Santa Clara, CA, United States

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Job details

Posting language: English
Posted: Nov 13, 2026

Original job description

Company Description Who we are Moveworks: the Agentic AI Assistant platform that empowers the entire workforce. Our platform enables employees to converse with all of their business systems through natural language to quickly find answers and automate tasks. Powered by the world's most advanced LLMs, our proprietary models, and a sophisticated Agentic AI platform, we're transforming how work gets done by allowing AI to take initiative, streamline complex workflows, and continuously learn and adapt. Moveworks is trusted by over 5.5 million employees at more than 350 of the world’s largest companies, including 10% of the Fortune 500, to automate everyday tasks and streamline business operations. Recognized on the Forbes Cloud 100 and AI 50 lists, Moveworks was also named one of Fast Company’s 2025 Most Innovative Companies and Inc’s Best in Business, in the Best in Innovation category. Moveworks was also recognized at Microsoft’s 2025 Partner of the Year and in 2024, received the AI Breakthrough Award. In December 2025, Moveworks was acquired by ServiceNow, marking a pivotal milestone in our journey to create a single front door to work for all business systems. By combining ServiceNow’s leading workflow automation with Moveworks’ Reasoning Engine and natural language capabilities, we deliver the AI platform for every person and every workflow. Built to go beyond basic summaries to deliver meaningful business impact. Together, our AI acts across enterprise systems to turn conversations into completed work. By joining our team, you’ll be at the forefront of the AI transformation, backed by the global scale of ServiceNow and the agility of a high-growth company. We are looking for world-class talent to help us extend agentic AI to every employee across every corner of the business. Come join us! ServiceNow: it all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today - ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.

Job Description

The Role Moveworks' AI agents don't just generate text - they act. They plan, call tools, and change real state in enterprise systems on behalf of 5.5 million employees. Building agents is no longer the bottleneck. Improving them is: knowing whether a change made the agent better, why a conversation failed, and which part of the system should be fixed. You'll build the loop that makes our agents improve themselves - finding failures in real use, deciding which part of the agent harness is responsible, checking every proposed fix on cases the fixer never saw, and turning each fixed failure into a permanent test. We improve the system around the model: instructions, tool descriptions, routing, knowledge and memory. We don't train the model itself. The work is blend of ML and systems engineering. You'll decide whether a signal is trustworthy enough to act on, and build the infrastructure that produces that signal reproducibly across hundreds of customer organizations. This isn't a model-training role, and it isn't a testing role. It's applied ML at a point where the methodology genuinely isn't settled: LLMs judging LLMs is an open research problem, and we're working it against agents that take real, irreversible actions in stateful, multi-tenant enterprise environments.

What you get to do in this role:

Judge design and calibration

A shared base judge with per-item rubrics expressed as configuration next to the dataset - so eval authors express intent, rather than forking a prompt per eval
Splitting the problem correctly: deterministic validators for checkable world state ("was the ticket created, with the right item, routed to the right approver?"), and an LLM judge for the parts that are genuinely fuzzy - was the clarifying question appropriate, was policy followed, was the path efficient
Scoring that reports its own confidence, so uncertain judgements route to a human instead of quietly becoming trusted signal
A standing calibration loop against human-labeled trajectories, run in partnership with our annotation team - they own the human labeling, you own the calibrated judge artifact. How consistently humans agree with each other sets the ceiling on how good any judge can be, so raising that ceiling is part of the job
Guarding against correlated blind spots: our user simulator and our judge are both LLMs, and they can be wrong in the same direction
Offline↔online divergence: when simulation and production disagree, being the person who can say why, and keeping the suite re-seeded from new production failures so it can't quietly overfit

Self-improving agent harness This is where the pillar is headed, and a large part of why the seat exists. You'll build the components that let the agent harness get better from its own use, without changing the model underneath.

Finding what to improve: surfacing where the agent falls short in real use, including failures that look like success
Synthetic data and environments: generating scenarios and the seeded enterprise worlds they run in - with expected outcomes and checks - from real failures or ahead of what customers ask for, and making sure what gets generated is realistic, hard enough, and doesn't leak the answer
Deciding where a fix belongs: understanding why the agent failed and which part of the harness should change: its instructions, tools, routing, knowledge or memory
Proposing improvements: optimizing those components against simulation rather than against production traffic
Checking before anything ships: verifying every proposed change on cases it wasn't tuned on, catching regressions an average would hide, and keeping the part that proposes a change separate from the part that judges it
Evals that grow: every fixed failure becomes a permanent test, so the suite gets stronger the more the agent is used
Safe across many customers: learnings stay scoped to one organization where they must, and admins approve anything org-specific
The infrastructure that runs it: a loop that runs reproducibly, at scale, across hundreds of customer organizations
The guardrail that keeps it honest: end-state outcomes are what we hold the agent to

Qualifications

To be successful in this role you have:

2+ years in applied ML, data science, or ML-adjacent engineering, with a track record of work that shipped and got used
Experience turning subjective human judgement into a measurement that holds up - one that other people, and ideally other models, can act on. This is the core of the job
Strong applied ML fundamentals, and comfort treating LLMs as a component you evaluate, prompt, and fine-tune rather than one you pretrain
Strong Python, and the discipline to ship production-grade code rather than notebooks
Ability to think and communicate clearly about complex problems - a large part of this job is convincing engineers that a number means what you say it means, and being right
A high degree of ownership and a bias toward shipping at startup pace
Comfort with ambiguity, and the judgement to know when a measurement is good enough to act on

Experience in at least 3 of these:

LLM-as-judge or automated evaluation design, and calibrating it against human judgement
Human annotation programs: rubric authoring, label quality, and annotator throughput as a real constraint
Search ranking, recsys, or online experimentation evaluation - golden-set staleness, offline/online divergence, side-by-side rater agreement. This is the closest existing analog to agentic eval, and it transfers directly
Fine-tuning and evaluating small models: SFT, preference tuning, distillation
Reward modeling, RLHF/RLAIF, or process reward models
Agent trajectory analysis and step-level fault attribution
Prompt engineering as an engineering discipline - versioned, tested, and measured, not tuned by vibes

For positions in this location, we offer a base pay of $161,300 - $274,200, 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.

Additional Information 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, creed, religion, sex, sexual orientation, national origin or nationality, ancestry, age, disability, gender identity or expression, marital status, 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. ©2025 Fortune Media IP Limited. All rights reserved. Used under license.

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Jobs in the United States