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Salary: USD 139,700 - 158,900 / annual
ServiceNow's Multimodal team is seeking a Machine Learning Engineer to design, build, deploy, and operate services that power multimodal AI capabilities—helping the platform understand documents, images, and videos in enterprise workflows.
In this role, you will:
• 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. Contribute to CI/CD, observability, and alerting infrastructure to keep systems reliable.
• Own quality and reliability in production: Write clean, tested code. Build automated tests, monitor service health, and investigate and resolve customer-facing issues including 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.
The role requires someone who cares deeply about building production-ready ML services: designing clean APIs and pipelines, deploying and scaling services reliably, and keeping them fast, observable, and resilient. You will own the quality and correctness of what ships, whether the code was written by a human or with the help of AI coding agents.
REQUIREMENTS:
• Master's degree in Computer Science, Machine Learning, or a related technical field, with 1 to 3 years of related experience.
• Experience leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving (using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact).
• 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 of computer vision techniques and ability to evaluate model quality independently, including designing test sets and choosing appropriate 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 track record of using AI tools to improve engineering workflows.
• Growth mindset: Eagerness to learn, take ownership, and grow in a collaborative team.
