About the Role
Lead the design and implementation of machine learning models integrated into VoIP systems to enhance call quality, detect anomalies, and optimize network performance at scale.
Responsibilities
- Architect scalable machine learning pipelines tailored for real-time voice communication data.
- Collaborate with infrastructure teams to embed AI-driven insights into VoIP monitoring tools.
- Develop models to predict and mitigate call quality degradation across distributed networks.
- Translate complex audio and network telemetry into actionable ML-ready datasets.
- Drive research initiatives to identify emerging ML techniques applicable to voice systems.
- Evaluate model performance under dynamic network conditions and high traffic loads.
- Ensure compliance with latency and reliability requirements in production environments.
- Mentor engineers working on ML integration within communication platforms.
- Optimize inference efficiency for low-latency voice processing workflows.
- Work closely with data scientists to align model outputs with operational KPIs.
- Design automated feedback loops for continuous model retraining and improvement.
- Troubleshoot end-to-end system behavior when integrating ML components.
- Maintain documentation for models, training processes, and deployment pipelines.
- Advocate for best practices in model explainability and monitoring.
- Support incident response related to ML system behavior in production.
- Contribute to cross-team technical standards for AI in real-time systems.
- Evaluate third-party tools and frameworks for potential adoption in ML workflows.
- Ensure models operate within privacy and data governance policies.
- Collaborate on root cause analysis of systemic VoIP performance issues.
- Deliver technical roadmaps for evolving ML capabilities in communication services.
- Participate in code and design reviews across ML and infrastructure teams.
- Integrate security principles into ML model development and deployment.
- Assess scalability of solutions across global user bases.
- Balance innovation with operational stability in high-availability environments.
- Drive adoption of ML-based diagnostics among operations and support teams.
Compensation
Competitive salary and comprehensive benefits package commensurate with experience.
Work Arrangement
Hybrid work model combining remote flexibility with periodic on-site collaboration.
Team
Part of a high-impact engineering team building intelligent systems for global communication platforms.
Why This Role Matters
Voice communication is a critical enterprise function, and machine learning is key to ensuring consistent quality and reliability. This role directly shapes how AI improves real-time interactions across global organizations.
What You’ll Bring
Deep expertise in machine learning applied to real-time systems, a passion for solving complex infrastructure challenges, and a collaborative mindset to drive innovation across engineering domains.
Sponsorship available for qualified candidates requiring work authorization.
