Staff Product Manager, AI Evaluations

Dropbox is seeking a Staff Product Manager to lead AI Evaluations (Evals) — the systems, metrics, and processes that measure the quality and reliability of AI-powered features across Dropbox. In this role, you’ll define how we evaluate model performance, accuracy, and user satisfaction across diverse AI surfaces like Dash, search, summarization, and intelligent organization.

You’ll collaborate closely with Applied AI, Data Science, and Research to design frameworks that ensure our AI features are helpful, safe, and high-quality. This includes everything from defining success metrics for model improvements, to building scalable pipelines that assess qualitative and quantitative signals at scale.

This role sits at the intersection of AI systems, data rigor, and product judgment — ideal for a PM who loves turning ambiguity into measurable progress and ensuring that every AI interaction meets a bar of excellence.

• Define and drive the roadmap for Dropbox’s AI Evaluation Framework, covering both quantitative metrics and human-in-the-loop systems.
• Partner with ML, data, and research teams to build evaluation pipelines that measure model accuracy, helpfulness, bias, latency, and hallucination rates.
• Translate business and product goals into measurable AI performance objectives.
• Develop taxonomies and methodologies for evaluating large language model (LLM) behaviors in production.
• Work closely with PMs across Dash to integrate eval signals into product development loops.
• Partner with infra and AI platform teams to operationalize eval tooling and dashboards.
• Ensure evaluations are ethical, transparent, and aligned with Dropbox’s privacy and trust standards.
• Communicate findings and drive accountability across teams for improving AI quality.

• 10+ years of product management experience, ideally in AI/ML systems, data platforms, or experimentation.
• Proven experience defining and shipping metrics-driven AI products or frameworks.
• Strong understanding of model evaluation concepts such as precision/recall, win-rate testing, and user feedback loops.
• Excellent analytical, problem-solving, and communication skills.
• Strong cross-functional collaboration across ML, data, design, and research teams.
• Comfort balancing experimentation speed with rigor and reproducibility.

• Background in ML, data science, or statistics.
• Experience building or managing human evaluation systems (e.g., annotation pipelines, Reinforcement Learning from Human Feedback).
• Familiarity with LLM behavior analysis, bias evaluation, and quality measurement frameworks.
• Deep curiosity about how AI systems can be made safer, fairer, and more reliable.