The Full Stack Engineer II will build and manage the end-to-end AI Crown product experience, collaborating closely with the AI research team and overseeing the application layer and data infrastructure.

Lead the adoption of AI tools across business functions at EviSmart by mapping workflows, developing training resources, and driving usage within teams.

Lead the end-to-end technical design of the Customer Experience Platform, overseeing engineers, and ensuring high uptime and scalability while integrating AI components.

Design intuitive user interfaces for a SaaS product, collaborating with teams to translate user needs into effective design solutions, while maintaining a design system.

The Account Executive is responsible for managing the full sales cycle, diagnosing workflow breakdowns, creating tailored proposals, and expanding accounts post-close. They will work directly with lab owners to identify automation opportunities that enhance operational efficiency using EviSmart's AI-powered platform.

The Product Manager will lead the development of EviSmart's AI-based dental crown design product, engaging with customers and integrating feedback to create user-friendly solutions, while coordinating with the ML team to ensure alignment between product and model capabilities.

Lead the frontend architecture for a new dashboard platform, establish engineering best practices, and guide junior engineers in development processes.

Lead automation and AI integration across dental workflows, designing and building solutions, and ensuring data integrity to improve processes and decision-making.

The VP, Go-To-Market will architect and own the integration of Sales, RevOps, Customer Success, and Digital Marketing, driving revenue growth and overall strategy. Responsibilities include managing company revenue and EBITDA, building a digital demand engine, and optimizing customer acquisition costs. This role requires building a GTM architecture and reporting directly to the CEO.

The ML Ops role focuses on building and managing infrastructure for deploying AI models in production, ensuring scalability, performance, and reliability. Key responsibilities include optimizing GPU workloads, designing CI/CD pipelines, and implementing observability for their platform.