Navigate AI

Product Manager

• Own model evaluation and quality standards
• Take full ownership of all evaluation systems, validation sets and quality metrics
• Establish and maintain “golden sets” for every product as the source of truth for quality and accuracy
• Drive measurable accuracy improvements
• Lead accuracy initiatives across all products, improving precision, recall, and overall model performance
• Define and track AI performance metrics and client SLAs as we scale
• Automate quality workflows
• Identify manual QA and data entry processes and redesign them through automation
• Reduce human intervention by increasing model confidence, reliability, and repeatability
• Build scalable evaluation pipelines
• Create repeatable processes to evaluate pilots and active customers quickly
• Ensure accuracy metrics remain high as customer volume and product surface area grow
• Close the real-world feedback loop
• Capture and analyze real-world model errors
• Prioritize accuracy issues and partner with Engineering to drive continuous model improvement