Technical Instructional Designer (AI Programmes)
Multiverse
| Company | Multiverse |
| Category | Data & Analytics |
| Location | London |
| Remote | Hybrid |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 21 Jul 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
Multiverse is the upskilling platform for AI and Tech adoption.
We have partnered with 1,500+ companies to deliver a new kind of learning that's transforming today’s workforce.
Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance.
In April 2026, we announced $70 million in strategic funding, led by Schroders Capital, with participation from StepStone Group, Lightspeed Venture Partners and General Catalyst. At an increased valuation of $2.1bn, the round makes us Europe’s first EdTech double unicorn.
But we aren’t stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We’re building a world where tech skills unlock people’s potential and output.
Join Multiverse and power our mission to equip the workforce to win in the AI era.
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
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