Senior ML Ops Engineer

Overview

In this role you will own end-to-end ML pipelines from data to deployment, collaborating with audio ML researchers and DSP engineers to productionize models at scale. You will implement monitoring, CI/CD, and reproducibility across the full model lifecycle. You will also help establish engineering standards within a small ML team and mentor others. This position sits at the intersection of real-time audio processing and scalable ML infrastructure, enabling high-quality sound enhancements across cloud, edge, and embedded environments.

Responsibilities

• Own end-to-end ML pipelines from data preparation to training, evaluation, and deployment
• Collaborate with audio ML researchers and DSP engineers to productionize models
• Implement monitoring and observability for ML systems
• Build and maintain CI/CD pipelines and reproducibility practices across the model lifecycle
• Mentor team members and help set engineering standards within a small ML team

Key requirements

• Proven experience building and operating production ML systems
• Strong Python and ML frameworks (PyTorch, TensorFlow)
• MLOps, CI/CD and model lifecycle management
• Cloud platforms (AWS, GCP or Azure) and containers / Kubernetes
• Experience with low-latency or real-time systems; familiarity with audio or speech processing a plus
• Mentoring or technical leadership experience desirable

• mentoring
• cross-functional collaboration
• effective communication
• Python
• PyTorch
• TensorFlow

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