Senior Staff Machine Learning Engineer

ServiceNow ServiceNow · Enterprise · Hartford, CT +1 · Engineering, Infrastructure and Operations

Senior Staff Machine Learning Engineer role focused on designing, developing, and implementing VoIP infrastructure and AI-driven voice workloads. This role involves integrating LLMs into real-time communication systems, prompt engineering, and contributing to the AI platform and AI-powered work experiences. The engineer will collaborate with various teams to ensure performance, scalability, and seamless integration of voice and AI platforms.

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 continuous improvement of the SRE practice by turning operational telephony and AI workload use cases into requirements for software tooling.
  4. Contribute to the execution of deployment and support activities for VoIP systems and AI/ML developers operating in production voice environments.
  5. Build high-quality, clean, scalable and reusable code by enforcing best practices around software engineering architecture and processes (Code Reviews, Unit testing, etc.).

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
  • 8+ 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

  • Experience integrating LLMs into voice platforms and real-time communication systems
  • Experience in prompt engineering and developing LLM based features

Other signals

  • integrating LLMs into voice platforms
  • prompt engineering and developing LLM based features
  • integrating AI into work processes