ServiceNow is seeking a Principal Machine Learning Engineer for its VoIP Infrastructure team in Santa Clara, focusing on designing and implementing AI-driven voice solutions. The role requires extensive experience in VoIP systems, programming, and operating distributed workloads, with an emphasis on collaboration and mentorship
Job Summary
You will play a major part in building AI and Machine Learning (ML) solutions that transform the user experience and workflow efficiency of enterprise services.
Lead the design and implementation of VoIP and CCaaS integrations, connecting ServiceNow's AI platform with enterprise contact center providers.
Be a mentor for colleagues and help promote knowledge-sharing across telecom and AI engineering disciplines.
Matching Summary
Match Score: 85
ServiceNow is seeking a Principal Machine Learning Engineer for its VoIP Infrastructure team in Santa Clara, focusing on designing and implementing AI-driven voice solutions. The role requires extensive experience in VoIP systems, programming, and operating distributed workloads, with an emphasis on collaboration and mentorship.
Salary
Base: $236,000 - $413,000; Bonus/Equity: equity (when applicable), variable/incentive compensation; Benefits: health plans, 401(k) Plan with company match, ESPP, matching donations, flexible time away plan, family leave programs
Skills & Requirements
Must-have
VoIP systems using SIP/RTP protocols
Kamailio, RTPEngine, FreeSWITCH
LLM integration layers
Prompt engineering and LLM features
Kubernetes DevOps approach
Cloud-native distributed systems
Nice-to-have
Continuous improvement of SRE practice
Promote knowledge-sharing across disciplines
Leveraging AI into work processes
Using AI productivity tools
Key Requirements
10+ years of development experience
10+ years of experience operating distributed workloads
10+ years of experience with infrastructure and platform operations
Extensive experience building VoIP systems
Practical knowledge of telecom systems
Working knowledge of PSTN infrastructure
Experience integrating applications on top of LLMs