ServiceNow

Senior Machine Learning Engineer

Join ServiceNow as a Senior Machine Learning Engineer in Santa Clara, CA. Leverage ServiceNow for ML model deployment, requiring 5+ years in Python and MLOps. Competitive pay and benefits.

ServiceNow Role Type:
ServiceNow Modules:
Department - JobBoardly X Webflow Template
Integration Hub
Department - JobBoardly X Webflow Template
Predictive Intelligence
ServiceNow Certifications (nice to have):

Job description

Date - JobBoardly X Webflow Template
Posted on:
 
June 26, 2025

Senior Machine Learning Engineer job description at ServiceNow: analyze data, train/test/deploy/maintain complex ML/AI/GenAI models, build ML/AI enabled application on the ServiceNow platform.

Requirements

  • Typically requires a minimum of 5 years of related experience.
  • Strong analytical skills with a focus on data-driven decision making.
  • Proficiency in Python and familiarity with its ecosystem of data science and machine learning libraries.
  • Demonstrated experience delivering scalable machine learning, deep learning, or generative AI solutions.
  • Solid understanding of MLOps practices, ideally with hands-on experience using Azure ML services.
  • Experience working with modern data platforms such as Snowflake.
  • Ability to quickly learn and apply new technologies, including the ServiceNow platform.
  • Strong grasp of software design and integration patterns, data modeling principles, and industry best practices.
  • A proven track record of successfully delivering complex, cross-functional projects.
  • Excellent debugging, testing, and problem-solving capabilities.

Benefits

  • Base pay of $158,500 - $269,500, plus equity (when applicable), variable/incentive compensation and benefits.
  • 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.

Requirements Summary

5+ years of experience, strong analytical skills, proficiency in Python, demonstrated experience with machine learning and MLOps practices