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We're looking for a sharp, high-energy generalist to become the connective tissue between our growing open-source community and our commercial pipeline. ZenML is transforming how AI teams build production-ready ML pipelines, and we have a problem most startups would kill for: thousands of engaged developers using our tool every month — but no one systematically turning that engagement into conversations. This isn't a traditional sales or marketing role. You won't be cold-calling from a list or writing ad copy. You will be the person who spots an ML engineer asking about pipeline orchestration in our Slack, reaches out with genuine help, and turns that into a product conversation. You will build the system that converts community warmth into commercial momentum. You’ll also play a key role in guiding new customers through onboarding and success, often getting hands-on with the product as you help them deploy and adopt ZenML effectively
Job Responsibility:
Warm Signal Detection & Outreach: monitor community channels daily to identify users and prospects showing buying signals, craft personalized, technical outreach
Inbound Qualification & Demo Booking: first point of contact for inbound interest, qualify leads, understand their MLOps pain points, book demos
Customer Onboarding & Success: take ownership of the early customer journey, collaborate with Solutions Engineering and Product to deliver smooth onboarding, proactive support, and early wins, partner with Solutions Engineering to debug common onboarding issues
Own “first value”: configure lightweight, tailored ZenML demos or example pipelines, design and execute simple “Day 1 / Week 1” success plans
Hands-On Technical Engagement: learn and use ZenML directly to help customers configure, troubleshoot, and succeed with the product
Event & Community Activation: represent ZenML at meetups, conferences, and webinars, help organize events
GTM Operations & Experimentation: help build the playbook, test, learn, and document
Partner & Ecosystem Outreach: identify and nurture relationships with complementary tools, consultancies, and communities
Requirements:
The Signal Spotter: instinct for noticing when someone is ready to talk
Comfort with code & infra: comfortable reading basic Python, YAML, or CLI output, and discussing CI/CD, containers, and cloud environments at a conceptual level
Technical Curiosity: genuinely interested in how ML teams work
Builder Over Bureaucrat: doesn't wait for a process to exist before acting
Communication: writes clearly and concisely, comfortable talking to senior ML engineers and CTOs
Hustle with Taste: moves fast but not sloppy, outreach is personalized, follow-ups are timely