Staff Machine Learning Engineer

ServiceNow ServiceNow · Enterprise · Toronto, ON +1 · Engineering, Infrastructure and Operations

Staff Machine Learning Engineer focused on VoIP infrastructure to power AI-driven voice workloads. This role involves designing, developing, and implementing telephony platforms and observability features, collaborating with engineering and product teams, and contributing to SRE practices. The engineer will build scalable code, own features from design to delivery, and integrate LLMs into real-time communication systems, acting as a mentor.

What you'd actually do

  1. Contribute to the design, development and implementation of VoIP infrastructure, telephony platforms, and observability features that power AI-driven voice workloads
  2. 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.
  3. Contribute to the execution of deployment and support activities for VoIP systems and AI/ML developers operating in production voice environments.
  4. 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.
  5. Be a mentor for colleagues and help promote knowledge-sharing across telecom and AI engineering disciplines.

Skills

Required

  • 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

Nice to have

  • 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

What the JD emphasized

  • integrating LLMs into voice platforms
  • prompt engineering
  • developing LLM based features
  • integrating applications on top of LLMs

Other signals

  • integrating LLMs into voice platforms
  • prompt engineering
  • developing LLM based features
  • integrating applications on top of LLMs