AI Product Manager, AI Platform - Quality and Evaluation

Posted: 2026-08-05

Are you passionate about building AI products that customers can trust by making quality measurable rather than subjective? Do you enjoy partnering with engineers and researchers to evaluate large language models, improve AI system performance, and turn data into product decisions? Are you excited by the opportunity to define the future of AI quality, observability, and evaluation platforms that power intelligent applications? If so, we invite you to be a part of our innovative team.

As a Product Manager on Ridgeline's AI Platform team, you'll define and execute the strategy for the systems that ensure our AI capabilities are accurate, reliable, and continuously improving. You'll lead initiatives spanning LLM evaluation frameworks, AI model benchmarking, evaluation harnesses, prompt and model experimentation, AI quality platforms, and observability capabilities that help teams understand, measure, and improve production AI performance. You'll work closely with engineering, AI researchers, design, and product teams to deliver platform capabilities that enable every AI-powered experience across Ridgeline. You'll leverage cutting-edge AI technologies and tools in a fast-moving, creative, progressive work environment while helping establish best practices for developing and operating enterprise AI products.

At Ridgeline, how we work matters as much as what we build. Ridgeliners act like owners, choose growth over comfort, and communicate with transparency. We assume positive intent, bias toward action, and bring solutions—not just problems. We celebrate wins, learn from setbacks, and thrive in a resilient, collaborative, high-performing culture. If this excites you, we’d love to meet you!

You must be work authorized in the United States without the need for employer sponsorship.

The impact you will have

- Define and execute the product strategy and roadmap for Ridgeline's AI Quality Platform

- Build scalable LLM evaluation frameworks and automated evaluation harnesses that enable continuous model validation

- Establish standardized offline and online evaluation methodologies, golden datasets, and quality benchmarks across AI products

- Define the roadmap for AI observability, enabling teams to monitor quality, latency, cost, reliability, and user outcomes in production

- Partner with engineering and AI researchers to build continuous regression testing and experimentation capabilities for generative AI systems