AI Automation Intern

AlgaeCal•Vancouver, BC

18h

About The Position

Want to get dangerously good at AI automation before everyone else catches up? As our AI Automation Intern, you’ll get a front-row, hands-on education in how a fast-growing health brand identifies repetitive work, builds practical AI systems, and turns cutting-edge technology into real operational leverage. You won’t just “play around with ChatGPT.” You’ll work on real automation projects that save time, reduce manual work, improve workflows, and help teams across the company operate faster and smarter. In this once-in-a-lifetime opportunity, you’ll learn how to: Identify high-impact automation opportunities across a business. Build practical AI agents using low-code and no-code tools. Improve workflows with prompt engineering and AI systems. Translate business problems into working AI solutions. Evaluate AI tools based on ROI, not hype. Help shape the future AI strategy of a growing company. This isn’t just a “sit in meetings and take notes” internship. If you’ve got the chops, it’s a launchpad into a lifetime of success. At AlgaeCal, we’re driven by a single idea. To end the fear of bone loss. In the United States, an estimated 54 million people have low bone density. The good news? AlgaeCal has the world’s only clinically-backed natural solution to this problem. We give hope to everyone worried about bone loss. If that excites you, you’ll find no better place to continue your career than at AlgaeCal.

Requirements

You’re the kind of person who sees repetitive work and immediately thinks: “There has to be a better way to do this.”
You’re fascinated by how AI can improve the way businesses operate in the real world. Not just flashy demos. Not just “cool prompts.” Real systems that save time, reduce friction, and make people more effective at their jobs.
You’ve probably already spent hours experimenting with AI tools on your own. Building automations. Testing prompts. Connecting workflows. Breaking things. Fixing them.
Maybe you’ve built AI agents, automated reporting systems, internal copilots, or workflows using tools like Zapier, Make, or n8n.
Maybe you’ve explored OpenAI APIs, Claude, vector databases, RAG systems, or orchestration frameworks because you genuinely wanted to understand how they work.
You enjoy solving messy problems.
You’re comfortable navigating both technical systems and human workflows.
You understand that great AI implementation is not just about the technology — it’s about understanding the people using it, the data behind it, and the business problem it’s trying to solve.
You’re highly detail-oriented and naturally proactive.
When something breaks, you investigate.
When a workflow feels inefficient, you improve it.
When you learn something useful, you look for ways to apply it elsewhere.
You want to build real things that actually matter.

Responsibilities

Identify high-impact automation opportunities across a business.
Build practical AI agents using low-code and no-code tools.
Improve workflows with prompt engineering and AI systems.
Translate business problems into working AI solutions.
Evaluate AI tools based on ROI, not hype.
Help shape the future AI strategy of a growing company.
Work directly with teams across the business to uncover repetitive workflows, bottlenecks, and manual processes that can be streamlined with AI.
Separate “interesting ideas” from automations that actually create measurable ROI.
Design and deploy practical AI agents using low-code and no-code tools.
Integrate real systems into existing workflows and help teams save time immediately.
Deepen your understanding of prompt engineering, retrieval-augmented generation (RAG), knowledge systems, and multi-agent workflows.
Learn how modern AI systems are structured — and where they break.
Become the bridge between operational teams and technical implementation.
Learn how to turn vague requests into clear workflows, practical automations, and deployable systems.
Help maintain a transformation dashboard tracking hours saved, efficiency gains, and operational improvements across AI initiatives.
Learn how successful companies evaluate AI beyond hype and headlines.
Learn how to diagnose workflow failures, improve agent behavior, refine prompts, and increase reliability over time.
Understand why great AI systems require iteration, not magic.
Develop a practical understanding of data governance, permissions, privacy concerns, and organizational risk management around AI systems.
Learn why responsible implementation matters just as much as innovation.
See how ambitious teams adopt AI to move faster, make better decisions, and eliminate low-value work — while maintaining high standards for execution, communication, and accountability.