Job description

ABOUT NAVIGATE AI Navigate is building frontier AI technology for the field workforce—starting with construction. We've partnered with some of the biggest real estate and construction companies in the U.S. and have early traction combining AI, spatial computing, and visual intelligence. We’ve only begun to unlock what’s possible when AI systems operate reliably in the real world. As our first AI Accuracy Product Manager, you’ll ensure our models perform with consistency, precision, and trust at scale. You’ll work closely with Product, Engineering, and Go-To-Market teams to define what “great” looks like for AI accuracy—and make it real. You’ll own the systems and processes that keep our AI Copilots dependable as they encounter messy, high-stakes customer data. Your work will shape how accuracy is measured, improved, and operationalized across every product we ship—turning raw model performance into outcomes our customers can rely on. WHAT YOU’LL DO 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 WHAT WE’RE LOOKING FOR Strong analytical foundation - Hypothesis-driven problem solver with deep comfort in data analysis and metrics - Understands statistical significance and how to curate representative validation datasets Bias toward execution and ownership - Thrives in ambiguity and moves quickly from problem to solution - Comfortable owning outcomes end-to-end and delivering results without perfect information Technical fluency (without needing to code) - Understands data pipelines, model behavior, and ML workflows - Can scope issues clearly and collaborate effectively with engineers - Proficient in SQL - Early adopter of vibe-coding tools AI-native mindset - Learns and adopts new AI tools quickly - Continuously improves workflows to maximize automation and leverage model capabilities Clear communicator and influencer - Translates complex accuracy metrics into clear, actionable insights - Aligns Product, Engineering, and Go-To-Market teams around quality standards and priorities WHY JOIN US? - Help define the future of AI Copilots in a huge market ripe for transformation - Massive ownership and impact as an early team member - Work with high-agency teammates and major enterprise customers - SF or NYC preferred

Requirements

Must-haves

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
Strong analytical foundation
Hypothesis-driven problem solver with deep comfort in data analysis and metrics
Understands statistical significance and how to curate representative validation datasets
Bias toward execution and ownership
Thrives in ambiguity and moves quickly from problem to solution
Comfortable owning outcomes end-to-end and delivering results without perfect information
Technical fluency (without needing to code)
Understands data pipelines, model behavior, and ML workflows

Nice-to-haves

SF or NYC preferred

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigomlsql

Platform: ashby
Effort: quick