Multimodal ML Engineer — Production‑Ready AI Services

Latitude; Santa Clara (CA); Hybrid; USD 140,000 - 159,000; Full time

Benefits offered by this job

Health plans

401(k) plan with company match

ESPP

Flexible time away plan

Family leave programs

Job summary

ServiceNow is seeking a Machine Learning Engineer to design, build, deploy, and operate services behind multimodal AI capabilities that understand documents, images, and videos. You’ll work on platform services bringing LLMs into scalable enterprise features, focusing on clean APIs, pipelines, and reliability in production.

The role emphasizes containerized deployments on Kubernetes, observability, and end-to-end ownership from API design through performance tuning and release.

Qualifications

• Master's degree in Computer Science, Machine Learning, or related field with 1–3 years of related experience.
• Experience integrating AI into workflows and evaluating AI-driven insights or automation.
• Strong fundamentals in data structures, algorithms, APIs, and testing; Java or JavaScript is a bonus.

Responsibilities

• Build scalable ML services and pipelines for document extraction, visual understanding, and agentic automation.
• Deploy and operate on Kubernetes; contribute to CI/CD, observability, and alerting for reliability.
• Write clean, tested code; build automated tests; monitor health and resolve customer-facing issues.
• Turn product requirements into end-to-end features from API design to data handling and tuning.
• Collaborate with product managers, engineers, designers, and consumer teams to define success.

Skills

Multimodal ML

Docker

Kubernetes

APIs

Testing

Java/JavaScript

System design

Observability

Education

Master's degree in CS/ML or related

Tools

Docker

Kubernetes

CI/CD

Job description

ServiceNow is seeking a Machine Learning Engineer to design, build, deploy, and operate services behind multimodal AI capabilities that understand documents, images, and videos. You’ll work on platform services bringing LLMs into scalable enterprise features, focusing on clean APIs, pipelines, and reliability in production.

The role emphasizes containerized deployments on Kubernetes, observability, and end-to-end ownership from API design through performance tuning and release.