Machine Learning Engineer

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, Service Now is the AI control tower for business reinvention. Our Service Now 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.

Join us to put AI to work for people.

The Multimodal team is transforming how Service Now 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.

The Machine Learning Engineer designs, builds, deploys, and operates the services behind Service Now'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.

• 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.

• 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…