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Salary: USD 176,100 - 308,200 / annual
ServiceNow is seeking a Staff Machine Learning Engineer to contribute to the design, development, and implementation of VoIP infrastructure, telephony platforms, and observability features that power AI-driven voice workloads. You will collaborate with engineering, product, and infrastructure teams to ensure voice and AI platforms perform efficiently, scale reliably, and integrate seamlessly across SIP/RTP, Kamailio, RTPEngine, and related telecom systems.
Key responsibilities include:
- Contributing to the continuous improvement of the SRE practice by translating operational telephony and AI workload use cases into software tooling requirements
- Executing deployment and support activities for VoIP systems and AI/ML developers operating in production voice environments
- Building high-quality, scalable, and reusable code by enforcing best practices around software engineering architecture and processes (code reviews, unit testing)
- Working with product owners to understand detailed requirements and owning code from design through implementation, test automation, and delivery—spanning both telephony infrastructure and LLM integration layers
- Mentoring colleagues and promoting knowledge-sharing across telecom and AI engineering disciplines
This is a senior individual contributor role with company-wide technical scope, requiring deep expertise in VoIP systems, AI/ML integration, and distributed infrastructure.
REQUIREMENTS:
- 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
- Assets: DevOps tooling (Helm, Ansible, Kubernetes, Prometheus, Splunk, GitLab CI); experience building software for compliance and security in regulated environments; 4+ years of infrastructure and platform operations, deployments, SRE, and DevOps with focus on platform health
