ServiceNow · Posted 2w ago
Senior Machine Learning Engineer (DevOps, Federal)
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇺🇸 Santa Clara, United States
- Contract
- Full-time
- Role
- ML engineering
- Source
- Stapply Data
Stack
- Python
- Go
- Kubernetes
- CI/CD
The posting
Join ServiceNow's Advanced Technology Group (ATG) as a Senior Machine Learning Engineer. In this role, you will build and evolve the AI platform, collaborate with product teams, and create AI/ML solutions that enhance user experience and workflow efficiency. You will work in a fast-paced environment, deploying and operating services in production Kubernetes environments, and owning observability for your builds. This position offers a range of benefits, including generous family leave, matched donations, and flexible PTO.
- Contribuer à la construction de solutions d'intelligence artificielle et d'apprentissage automatique qui transforment l'expérience utilisateur et l'efficacité des flux de travail des services d'entreprise. - Collaborer quotidiennement avec une équipe de développeurs, de chefs de produit et d'ingénieurs qualité pour produire des logiciels de qualité, en comprenant les exigences détaillées et en prenant en charge votre code depuis la conception jusqu'à la livraison. - Déployer, mettre à l'échelle et exploiter des services dans des environnements Kubernetes de production, y compris les déploiements basés sur Helm et les pipelines CI/CD.
- Experience building new products that use challenging algorithms - Knowledge of unit testing, profiling, and code tuning - Expertise in coding efficient, object-oriented, modularized and quality software - Expertise in Go or Python, Design Patterns, time and space-efficient algorithms - Knowledge of core AI/ML techniques and algorithms - Confident use of AI coding assistants such as GitHub Copilot or Cursor, paired with the critical eye to review AI-generated code as rigorously as any other — catching logic errors, security anti-patterns, and missed edge cases rather than treating model output as production-ready - AI-assisted debugging and trace analysis, and a clear understanding of the data privacy rules governing what goes into a prompt