Staff Machine Learning Engineer - Voip Infrastructure
ServiceNow
Santa Clara, United States
$173,100 - $303,000 base pyy; equity, variable/inc...
On-site
Voip systems using sip/rtp
Kamailio, rtpengine, freeswitch
Pstn infrastructure and telecom protocols
ServiceNow is seeking a Staff Machine Learning Engineer specializing in VoIP Infrastructure to design and implement telephony platforms that power AI-driven voice workloads. The ideal candidate will have extensive experience with VoIP systems, cloud-native software, and integrating LLMs into communication systems
Job Summary
Contribute to the design, development and implementation of VoIP infrastructure, telephony platforms, and observability features that power AI-driven voice workloads.
Collaborate with engineering, Product, and infrastructure teams to ensure our voice and AI platforms perform efficiently, scale reliably, and integrate seamlessly across SIP/RTP, Kamailio, RTPEngine, and related telecom systems.
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 Staff Machine Learning Engineer specializing in VoIP Infrastructure to design and implement telephony platforms that power AI-driven voice workloads. The ideal candidate will have extensive experience with VoIP systems, cloud-native software, and integrating LLMs into communication systems.
Salary
$173,100 - $303,000 base pay; equity, variable/incentive compensation; health plans, 401(k) Plan with company match, ESPP, matching donations, flexible time away plan and family leave programs
Skills & Requirements
Must-have
VoIP systems using SIP/RTP
Kamailio, RTPEngine, FreeSWITCH
PSTN infrastructure and telecom protocols
integrating applications on top of LLMs
prompt engineering and LLM based features
Python, GoLang, Java development experience
Kubernetes, DevOps approach
distributed systems with cloud-native software
Nice-to-have
software-defined networking
infrastructure as code
configuration management
DevOps tooling experience
compliance and security in regulated environments
leveraging AI into work processes
AI productivity tools
Key Requirements
4+ years of development experience
4+ years of experience operating distributed workloads on Kubernetes
4+ years of experience with infrastructure and platform operations