Multiverse is an edtech company founded in 2016 that started with digital apprenticeships and now provides enterprise AI training and reskilling.
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. WHY THIS ROLE EXISTS Every foundation model lab is solving the same problem: build more capability. Almost nobody is solving the harder one — what happens when that capability meets a real organisation and real people who have to relearn how they work. That's the adoption layer, still wide open, and Multiverse sits in the unique position to solve this problem with ten years of proprietary learner and employer data driven from 1,500+ companies that no one else has. Diagnose & Prescribe (D&P) is the engine room of that problem. It works out what a learner or employer actually needs, then decides what they get funded for and which apprenticeship or learning path they're pointed to next. D&P is how we assess a learner's or employer's skill gaps, decide what they need, and point them to the right apprenticeship or learning path — so the blast radius here is human and financial, not just a dashboard metric. The line between leading this team and building the product has already blurred, which is why this role exists as it does. We need a leader who stays close enough to the work to have credible judgment in it, and who builds a high-trust team where diverse perspectives actually make the problem-solving better. This is a broad, domain-coherent, event-driven and increasingly AI-native estate. The dominant challenge isn't deep algorithmic difficulty in any single repo — it's holding the whole D&P picture, sequencing work across varied services, and keeping load-bearing decisions safe as the system evolves underneath you. WHAT YOU'RE SIGNING UP FOR - A genuinely unsolved problem — the constraint here is people and adoption, not model capability. - High agency. We set outcomes, not playbooks. You and your team define how this evolves. - Real commercial breadth. This team sits at the intersection of Product, go-to-market, and eligibility — closer to the customer than most engineering roles. - Craft that's respected, not traded off. Strict typing, layered testing, ADRs, observability — and AI-native ways of working as the baseline, not an experiment. - A company mid-transformation, told straight. Multiverse is rebuilding itself into a product-powered, AI-native company in real time. You're not inheriting a finished system — you're one of the people defining it. WHAT YOU'LL DO You will own delivery, technical direction, and team health in the D&P service estate — the full learner and customer journey from diagnosis through prescription to onboarding and growth tracking. Concretely, that means leading the team(s) across surfaces like: - Diagnosis & needs assessment — AI-heavy services (retrieval-augmented generation (RAG), LLM-scored assessment) that turn raw signal into a picture of where a learner or employer actually is. - Prescription & eligibility — build the product which helps learners and customers understand the best learning course for their skills and career development, that leverages a customer's levy funds. Much of this is load-bearing logic with a genuine financial and regulatory blast radius. - Recommendation & onboarding — turning a diagnosis into the right prescribed path and getting learners into it, where those decisions become visible to customers and learners. - The platform arc — continuing to carve D&P logic out of the shared legacy monolith into dedicated services, while backing the newer AI-native product from beta toward scale. - Coach and build the team. Hire well, give every report a real growth plan, and move the team into a high-performing, AI-native operating rhythm — promotions and performance issues are never a surprise. - Stay hands-on. Whiteboard distributed designs, review and negotiate the RabbitMQ event schemas and API contracts that ripple across services, and write production code yourself when it's the fastest way to de-risk or unblock. - Deliver predictably. Use cycle time, change failure rate, and MTTR to balance shipping speed, debt, and change-safety on logic with genuine financial and regulatory blast radius. - Raise the AI-native bar. Prove better discovery-to-build-to-review loops on your own team and share what works across engineering. - Partner on strategy. Work directly with Product and Design — and closely with go-to-market — to turn learner and customer behaviour into technical direction on the surfaces where eligibility and recommendation decisions become visible to real customers. WHAT WE NEED A few terms you'll see below: D&P (Diagnose & Prescribe, this team's name), BAM (our hiring framework — Behaviour, Achievement, Mastery), ADRs (architecture decision records), IfATE (the UK body governing apprenticeship standards). - Engineering management of full-stack teams shipping production software, with real technical depth to review architecture and challenge design. - A team you led has shipped AI features to real users and operated them — not a side experiment. - Strong Python/TypeScript (if you don’