Toast

Principal Product Manager for Voice AI at Toast, focusing on defining strategy, evaluating vendors, leading zero-to-one discovery and pilots, and commercializing a reusable voice platform for in-store restaurant experiences. The role involves defining vision, running vendor evaluations, driving pilots, designing platform APIs, leading go-to-market strategy, and coordinating cross-functional delivery. Requires AI product delivery experience, hands-on AI model evaluation, zero-to-one experience, platform mindset, and customer/ops empathy.

Principal Product Manager for Toast's Agent Platform, defining and scaling the foundational layer for AI agents across the company. This role focuses on orchestration, tool integration, governance, evaluation, and observability to ensure a seamless and trustworthy AI experience for Toast IQ.

Principal Data Product Manager, Applied AI at Toast. Owns the agenda for AI-enriched data to improve customer experience understanding. Prototypes, validates, and scales AI-powered data products using NLP, ML, and LLM approaches for customer success. Focuses on contact classification, summarization, sentiment analysis, churn detection, and product mention extraction. Manages pipeline from problem framing to embedding generation, classifier training, and production. Partners with data engineering for production implementations and owns quality governance for AI products in production.

Principal ML Engineer to build 0-to-1 internal tools for sales teams, leveraging LLMs and agentic workflows to automate tasks like account research and lead prioritization. The role involves hands-on backend engineering and ML implementation, integrating with systems like Salesforce and custom quoting engines, and delivering recommendations to sales users. Focus is on building new intelligence models and agentic systems.

Senior Product Manager for Support AI Experimentation at Toast. This role focuses on scouting new AI technologies, designing and running experiments for AI-driven support concepts, making go/no-go decisions, managing an experimentation roadmap, and translating findings into business outcomes. The role requires a deep understanding of the AI landscape and the ability to operate in an ambiguous, evolving environment.