Machine Learning Engineer

About the Team
The Multimodal team is transforming how ServiceNow understands multimodal content, including documents, images, and videos, by bringing the latest advances in AI into enterprise workflows. We build and own the platform services and products that power use cases across document extraction, visual understanding, and agentic automation. Our team includes ML engineers and applied researchers who are passionate about turning cutting-edge research into reliable products that deliver real customer impact.
Job Description
The Machine Learning Engineer designs, builds, deploys, and operates the services behind ServiceNow's multimodal AI capabilities, helping the platform understand documents, images, and videos. The Engineer works on the platform services and products that bring LLMs into reliable, scalable enterprise features. This role needs someone who cares deeply about building production-ready ML services: designing clean APIs and pipelines, deploying and scaling services on Kubernetes, and keeping them fast, observable, and resilient. The Engineer owns the quality and correctness of what ships, whether the code was written by a human or with the help of AI coding agents.
What you get to do:
Build scalable ML services. Design, develop, and improve services and pipelines for document extraction, visual understanding, and agentic automation, integrating LLMs into production systems.
Deploy and operate on Kubernetes. Containerize, deploy, and scale services on Kubernetes, and contribute to CI/CD, observability, and alerting that keep them reliable.
Own quality and reliability in production. Write clean, tested code, build automated tests, monitor service health, and help investigate and resolve customer-facing issues such as performance limits and quality gaps.
Build product features end to end. Turn product requirements into well-designed features, from API design and data handling to performance tuning and release.
Collaborate across teams. Partner with product managers, engineers, designers and consuming product teams to define success criteria, understand tradeoffs, and communicate capabilities and limitations clearly.
Qualifications
Master's degree in Computer Science, Machine Learning, or a related technical field, with 1 to 3 years of related experience.
Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.
Software engineering fundamentals. Strong command of data structures, algorithms, system design, APIs, concurrency, and testing. Java or JavaScript experience is a bonus.
Hands-on experience with Docker and Kubernetes
ML foundations. Solid understanding of machine learning fundamentals and how LLMs and vision-language models are integrated into applications.
Computer vision and model evaluation. Understanding computer vision techniques and the ability to evaluate model quality independently, including designing test sets and choosing the right metrics.
Production mindset. Experience building, deploying, and operating services, with attention to scalability, observability, and reliability.
Hands-on multimodal experience. Projects, research, or work involving document understanding or multimodal models is a strong plus.
AI-native approach. Curiosity and a track record of using AI tools to improve engineering workflows.
Growth mindset. Eagerness to learn, take ownership, and grow in a collaborative team.
For positions in this location, we offer a base pay of $139,700 - $158,900, 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.

Employment type
Full-time

Work arrangement
Hybrid