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Salary: USD 158,900 - 178,100 / 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.
You will work on platform services and products that integrate large language models (LLMs) and vision-language models into reliable, scalable enterprise features. This role requires someone who cares deeply about production-ready ML systems: designing clean APIs and data pipelines, deploying and scaling services on Kubernetes, and maintaining fast, observable, and resilient infrastructure.
Key responsibilities:
- Build scalable ML services for document extraction, visual understanding, and agentic automation, integrating LLMs into production systems
- Deploy and operate services on Kubernetes; contribute to CI/CD, observability, and alerting infrastructure
- Own quality and reliability in production: write tested code, build automated tests, monitor service health, and investigate customer-facing issues
- Build product features end-to-end, from API design and data handling to performance tuning and release
- Collaborate with product managers, engineers, designers, and consuming teams to define success criteria and communicate capabilities and limitations
The team includes ML engineers and applied researchers passionate about turning cutting-edge research into reliable products that deliver real customer impact.
Requirements:
- Master's degree in Computer Science, Machine Learning, or related technical field, with 1–3 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)
- Strong software engineering fundamentals: data structures, algorithms, system design, APIs, concurrency, and testing (Java or JavaScript experience is a bonus)
- Hands-on experience with Docker and Kubernetes
- Solid understanding of machine learning fundamentals and how LLMs and vision-language models are integrated into applications
- 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
