Staff Machine Learning Engineer (VoIP Infrastructure)

• 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

Benefits

• Generous family leave
• Matched donations
• Annual learning stipends
• Flexible PTO
• Competitive retirement plan
• Paid volunteer time- 4+ years of development experience with Python, GoLang, Java or similar languages
• Experience with software-defined networking, infrastructure as code and configuration management
• 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
• Hands-on experience building VoIP systems using SIP/RTP protocols
• Experience integrating applications on top of LLMs (using existing models, not building them)
• Experience with DevOps tooling  (e.g. Helm / Ansible / Kubernetes / Prometheus /Splunk/ GitLab CI) is considered an asset
• Working experience building distributed systems with cloud-native software
• Experience in prompt engineering and developing LLM based features
• Experience building software for compliance and security in regulated environments is considered an asset
• 4+ years of experience operating highly available distributed workloads on Kubernetes following a DevOps approach
• Practical knowledge of Kamailio, RTPEngine, FreeSWITCH, SBCs, and PSTN systems (or similar)
• Working knowledge of PSTN infrastructure and telecom protocols