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AI Adoption Engineer

RemotoMidFull-time

Publicado 23 de junio de 2026

Oferta externa

Esta vacante proviene de una fuente externa. La descripción puede estar abreviada y algunos datos (salario, habilidades) pueden no estar disponibles. Regístrate como candidato para recibir la información completa.

Descripción del puesto

Our mission is to automate coding. The first step in our journey is to build the best tool for professional programmers, using a combination of inventive research, design, and engineering. Our organization is very flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code. About the role Buying Cursor is easy. Changing how a 5000-person engineering organization builds software is hard. This role bridges that gap. As an AI Adoption Engineer, you are the person who shows up after the contract is signed and makes adoption real. You design and run the hackathons, facilitate the Customer Developer Days, build the internal champions, and create the conditions for engineering teams to genuinely shift how they work with AI. You are not a trainer delivering slides. You are a practitioner who makes things happen in the room. This is a post-sales, customer-facing IC role. You will work closely with AI Deployment Managers and Customer Success to identify where adoption is stalling and design high-touch, experiential interventions that move it forward. Your success is measured by whether developers actually change how they build, not by how many events you run. What you’ll do Design, produce, and run internal hackathons within customer organizations: company-specific formats, cohort-based programs, single-day sprints, multi-day builds, and theme-based challenges Build and own the Customer Developer Day program: immersive half-day or full-day events designed for specific accounts or customer segments Develop the facilitation infrastructure that makes these programs repeatable: playbooks, judging rubrics, agenda frameworks, and post-event retrospective templates Partner with AI Deployment Managers and Customer Success to identify the right intervention for each account: which customers need a hackathon, which need a structured workshop, which need something else entirely Capture outcomes from every engagement and turn them into customer success stories for internal account teams and for broader Cursor storytelling Identify patterns across customer engagements and feed them back into how the broader Customer Education team designs programs and content Be a credible technical presence in the room with developers: you can follow a codebase, troubleshoot in real time, and demonstrate Cursor capabilities at a level that earns developer trust You may be a fit if You have experience running developer events, hackathons, or technical workshops and can point to adoption outcomes that followed You are technical enough to be credible with experienced engineers. You use Cursor or comparable AI coding tools in your own workflow and have genuine opinions about what good AI-assisted development looks like. You understand the difference between a great event and a great adoption program. Events are a mechanism. Behavior change is the goal. You are energized by being in the room with customers, not behind a content queue. You do your best work when the stakes are live. You have worked in a post-sales or customer success context and understand how to operate within account relationships without disrupting them You are a strong communicator and facilitator. You can hold the attention of a room of skeptical engineers and also sit across from a VP of Engineering and explain why this matters. You use AI tools as core infrastructure in how you work, not as a novelty Strong candidates may also have Prior experience in developer relations, developer advocacy, or technical customer success Background in instructional design, facilitation, or learning science Experience at a developer tools company: IDE, API platform, DevOps, or AI tooling Familiarity with enterprise engineering environments: how decisions get made, how rollouts happen, where adoption stalls Content creation experience: technical writing, video tutorials, or live coding sessions

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