Manager, Machine Learning

2 days ago

Irvine, CA, United States Convergenz Full time

Manager, Machine Learning
Lead the development and operationalization of machine learning systems powering the Voice AI experience. This role is central to ensuring performance, scalability, and reliability across real-time models that support speech, natural language understanding, and agent behavior.
Manage a team of MLEs and partner with AI Engineers, QA, and DevOps to deliver high-quality agent performance with a strong focus on latency, integration with restaurant systems (e.g., HME, POS), and production excellence. Key Responsibilities

ML System Design & Architecture
• Lead the design of end-to-end ML pipelines for speech, ASR, and NLU modules
• Optimize model performance for real-time interaction, including latency, uptime, and inference cost
• Implement and evolve model evaluation, testing, and monitoring frameworks
Infrastructure & Integration
• Collaborate with engineering to integrate ML components with external systems (HME, menu boards, POS)
• Support scalable deployment strategies across markets and environments
• Drive MLOps best practices in CI/CD, rollback, logging, and observability
Leadership & Collaboration
• Mentor and guide a team of MLEs and junior ML engineers
• Partner with product, AI engineering, and QA to define technical scope, delivery targets, and quality standards
• Support internal upskilling and technical review of AI-driven components Required Qualifications
• 6+ years of experience in machine learning, including at least 2 years in technical leadership roles
• Proven expertise in deploying NLP, ASR, or LLM-based systems in real-time applications
• Strong programming skills in Python and ML tooling (e.g., PyTorch, HuggingFace, ONNX, MLflow)
• Experience optimizing model latency and integrating ML with backend infrastructure Preferred Qualifications
• Experience in QSR, retail, or voice-driven customer service environments
• Background in integrating AI with physical systems (HME, POS, IoT)
• Familiarity with LoRA, quantization, distillation, and model compression techniques

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