AI Automation Co-op (Fall 2026)

Later is the world’s most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing’s most visible investments. Built on a native, AI-powered platform and more than a decade of proprietary data—including billions of social interactions, impressions, and $2. 4B+ in verified influencer-driven purchases—Later helps teams understand what will work before they launch. By combining trusted insight with expert guidance, Later removes guesswork from influencer marketing, enabling brands to choose the right creators, execute fully managed campaigns, and drive meaningful growth across awareness, engagement, and revenue. Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good—they deliver results. Learn more at later. com . About this position: We're looking for an AI Automation Engineer Co-op to embed with teams across Later and help replace manual, repetitive work with AI-powered tooling. This is a hands-on role at the intersection of building and learning: you'll contribute directly to automations that create real business impact across Operations, Finance, Marketing, Customer Success, and beyond while being supported and coached throughout. You'll work closely with Later's AI Automation team and get direct exposure to how AI tools, APIs, and no-code platforms come together to solve real workflow problems across Later's three lines of business (Later Influence, Later Social, and Mavely). If you're curious about applied AI, love tinkering with new tools, and want to leave a term with a portfolio of things you actually shipped, this is the role for you.   What you'll be doing: Discovery & Problem Scoping - Shadow and interview non-technical stakeholders across Operations, Finance, Marketing, and Customer Success to understand their day-to-day workflows. - Help identify repetitive, manual tasks that are strong candidates for automation.

- Contribute to translating fuzzy workflow problems into clear, scoped technical solutions with guidance from the team. Building & Shipping - Build working automations and internal tools using AI-native platforms, APIs, and no-code and low-code tools. - Ship v1 prototypes quickly so stakeholders can validate value before investment grows. - Wire together LLMs, agents, and prompt design patterns with existing systems to deliver real workflow outcomes. - Iterate on automations based on stakeholder feedback and real-world use. Documentation & Reliability - Write clear documentation so automations can run unattended and teammates can understand what was built. - Contribute to error handling and basic monitoring so automations recover gracefully. - Help maintain a library of reusable templates, prompt patterns, and integration examples. Enablement & Collaboration - Support working sessions and lightweight training that helps non-technical colleagues understand and use the automations you build. - Collaborate with engineers, operations teammates, and other stakeholders to deliver solutions that work in practice. - Share what you learn openly, contributing to Later's growing citizen-developer community. What success looks like: - By the end of your term, you have at least 2-3 automations in production with documented business impact (hours saved, error rates reduced, or cycle times shortened) across different teams. - Stakeholders can use and understand the automations you built without needing you to explain them every time, because you wrote documentation that actually works. - You've grown your fluency with AI-native tools, APIs, and no-code platforms and can talk clearly about the tradeoffs of different approaches.