Staff Machine Learning Engineer - VoIP Infrastructure

Company Description

It all started in sunny San Diego, California in 2004 when a visionary engineer, Fred Luddy, saw the potential to transform how we work. Fast forward to today — ServiceNow stands as a global market leader, bringing innovative AI-enhanced technology to over 8,100 customers, including 85% of the Fortune 500®. Our intelligent cloud-based platform seamlessly connects people, systems, and processes to empower organizations to find smarter, faster, and better ways to work. But this is just the beginning of our journey. Join us as we pursue our purpose to make the world work better for everyone.

Job Description

What you get to do in this role:

PLATO (Platform Engineering and AI Technology Organization) at ServiceNow is a customer-focused innovative group building intelligent software using a variety of technology stacks to enable end-to-end, industry-leading work experiences for our customers. We are a group of people deeply invested in the success of our customers that happen to have expertise and knowledge in advanced technologies and software engineering best practices. We are data driven, structured, committed and we enjoy what we are doing. We prioritize robustness, performance and user experience over the technology stack and tools.

We are a group of technology professionals and platform engineers with a dual mission. We build and evolve the AI platform, and partner with teams to build products and end-to-end AI-powered work experiences. In equal measure, we lay the foundations, research, experiment, and de-risk AI technologies that unlock new work experiences in the future.

As a Staff Machine Learning Engineer - VoIP Infrastructure you will:

• 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. 
• Contribute to the continuous improvement of the SRE practice by turning operational telephony and AI workload use cases into requirements for software tooling. 
• Contribute to the execution of deployment and support activities for VoIP systems and AI/ML developers operating in production voice environments. 
• Build high-quality, clean, scalable and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit testing, etc.). 
• Work with product owners to understand detailed requirements and own your code from design, implementation, test automation, and delivery — spanning both telephony infrastructure and LLM integration layers. 
• Experience integrating LLMs into voice platforms and real-time communication systems. 
• Be a mentor for colleagues and help promote knowledge-sharing across telecom and AI engineering disciplines. 

Qualifications

To be successful in this role you have:

• Hands-on experience building VoIP systems using SIP/RTP protocols;   
• Practical knowledge of Kamailio, RTPEngine, FreeSWITCH, SBCs, and PSTN systems (or similar); 
• Working knowledge of PSTN infrastructure and telecom protocols; 
• Experience integrating applications on top of LLMs (using existing models, not building them); 
• Experience in prompt engineering and developing LLM based features  
• 4+ years of development experience with Python, GoLang, Java or similar languages.   
• 4+ years of experience operating highly available distributed workloads on Kubernetes following a DevOps approach.  
• Working experience building distributed systems with cloud-native software;  
• Experience with software-defined networking, infrastructure as code and configuration management; 
• Experience with DevOps tooling  (e.g. Helm / Ansible / Kubernetes / Prometheus /Splunk/ GitLab CI) is considered an asset 
• Experience building software for compliance and security in regulated environments is considered an asset 
• 4+ years of experience with infrastructure and platform operations, deployments, SRE, and DevOps with a continued focus on improving Platform health is considered an asset 
• Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact on the function or industry.  
• Experience in using AI productivity tools such as Cursor, Windsurf, etc  

For positions in this location, we offer a base pay of $173,100 - $303,000, plus equity (when applicable), variable/ince