Technical Instructional Designer (AI Programmes)

Job description

Every team has AI. Few have mastered it. Humans were built to adapt, but AI is now moving faster than most workforces can. Multiverse is the AI adoption layer: the missing link between the AI tools that companies have invested in and the people expected to use them. Our ambition is to put AI capability in every worker’s hands. Apprenticeships got us started: we’ve trained over a third of the UK’s data and AI apprentices, worked with a third of the FTSE 100, and generated £2bn+ in verified ROI. Now Mastry, our new adaptive learning platform, takes that further, guiding a company’s AI workforce transformation from start to finish. Mae, our AI guide inside Mastry, is a real-time AI agent that thinks, speaks, listens and sees, working alongside the tools you already use, like Claude and Copilot. We closed our latest fundraising round in April 2026, with $500m+ raised in total from VC partners including Index Ventures, General Catalyst, Schroders Capital and Lightspeed. With a $2.1bn valuation, we are Europe’s first EdTech double unicorn, and we are just getting started. If you want to shape how the workforce wins in the AI era, come and join us. THE OPPORTUNITY AI can draft a lesson in seconds. It can't tell you whether that lesson will actually land with a nervous career-switcher three weeks into a Data or AI apprenticeship — or whether the concept it just explained is subtly, but fundamentally, flawed. That's where you come in. You'll be the human judgement layer sitting on top of our AI-assisted content pipeline — part subject-matter expert, part instructional designer. You'll decide what AI got right, fix what it didn't, and build the parts no AI can: the high-touch, high-stakes, deeply human moments in a learner's journey that make the difference between content that's technically correct and content that actually changes someone's career. You'll have experience building content in generative and agentic AI, Data Analytics, and Data Science — not just someone who's read about it, but someone who can explain it, question it, and know when the AI-generated explanation of a neural network is actually going to help a learner or quietly confuse them. WHAT YOU'LL ACTUALLY DO - Design outcomes, not just content. Apply end-to-end backward design to build Data and AI programmes where success is measured by what learners can do on the job — not just what they can pass a test on. You'll own the hardest part: turning a job-aligned outcome into a curriculum that actually gets learners there. - Chase real-world impact, relentlessly. When a piece of learning isn't landing — when a Data Analyst cohort isn't showing the outcomes it should — you won't wait to be told. You'll spot it, own it, and scope the revisions that bring it up to the standard it needs to hit. - Build what AI can't. Design the custom, high-touch programmes and creative assets — branching narratives, bespoke multimedia, high-stakes onboarding moments — that need real human empathy, creativity, and nuance. If AI could've built it, it's not your job; if only a person could, it is. - Put AI to work, deliberately. Use AI tools and prompt engineering to accelerate your own content development and uncover learning design approaches nobody's tried yet. This isn't about using AI because it's there — it's about knowing exactly where it earns its place. - Advise up, not just build. Work directly with Programme Designers and senior stakeholders on the quality and pedagogical effectiveness of AI and human output. Your feedback doesn't stay in the room — it shapes how our AI rules and frameworks evolve. - Prototype for the long game. Lead new learning formats and modalities that don't exist yet — and know how to balance shipping fast against building something sustainable enough to reuse. WHAT YOU'LL BRING - Real command of end-to-end ADDIE and backward design — you've built measurable, job-aligned outcomes and the assessments that prove learners hit them, not just theoretical frameworks on a CV. - A habit of treating "good enough" as a starting point, not a finish line — you notice when learning isn't driving real outcomes, and you take the initiative to fix it. - Genuine fluency with AI-powered tools and prompt engineering, used to accelerate content development and find genuinely new applications in Data/AI learning design. - The ability to take a genuinely complex AI, Data Science, or Analytics concept and design a learning experience around it that actually works — for someone with zero technical background. - The confidence and track record to influence peers and senior stakeholders toward a specific, measurable curriculum improvement — not just raise a concern and move on. - Resilience when things don't go to plan — you can point to a time a project hit a real obstacle (a blocker, a shifting SME, a launch that missed the mark) and describe exactly what you did about it, and what you learned. WHAT GETS YOU IN