About the role
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 Multiverse is the UK’s largest apprenticeship provider and its first EdTech unicorn. The current state of AI presents a huge opportunity to reshape the future of education and workforce development — and Multiverse is in a uniquely strong position to do that. Getting it right has implications beyond the company: for the UK tech sector and the broader economy. The Scotland hub exists to make that real. A new engineering team with the mandate to build AI-native products, help modernise the existing platform, and set the practices that make Multiverse an AI-first company. Multiverse has built an environment where AI-native ways of working collapse the old boundaries, so one person can own the whole arc from idea to live product. As Principal AI Engineer, this is a deeply technical role. You’ll be the person other engineers turn to when the hard problems land — the one who navigates architectural ambiguity, makes high-stakes design decisions, and holds the bar on engineering quality across everything we build with AI. You ship code alongside
the team; this is a hands-on building role, not an advisory position. What You’ll Do Own the AI agent architecture. Design the orchestration layer, memory and context management, evaluation framework, and integration APIs that all agent products build on. Your decisions are the ones others build on top of. Ship production agents. You write and review code and own what goes to production. You’ll personally deliver at least one major agent system in your first six months. Set the engineering standard. Define how Multiverse builds with AI — evaluation methodology, multi-agent coordination patterns, tool design, guardrails, and observability. You author the decision records and hold the bar. Build the integration layer. Create the APIs, MCPs, and shared data contracts that connect agents to Multiverse’s platform, content systems, and internal tools — working closely with London engineering teams who own those systems today. Drive technical strategy. Translate product and business goals into a coherent AI engineering roadmap. Shape which problems we tackle, in what order, and why — then socialise it with engineering leadership and the exec team. Raise the bar around you. You’re not a line manager, but your presence makes the engineers you work with measurably better. Code review, pairing on hard problems, setting the standard for what ‘good’ looks like in AI-native engineering. What We’re Looking For Production AI Agent Engineering You’ve shipped multi-agent systems to real users. You understand context management, model selection and routing, cost engineering (token economics, caching, prompt optimisation), tool use and failure handling, multi-agent coordination, and evaluation frameworks for non-deterministic systems. This is depth, not familiarity. Technical Strategy and Influence at Scale You’ve set technical direction across multiple teams, not just within a single squad. You translate complex architecture decisions into business-relevant narratives for executive stakeholders. You’ve defined engineering standards that were adopted organisation-wide — not just recommended, but embedded. Full-Stack Delivery You work across the stack — LLM integration, backend services, data pipelines, and enough frontend to ship end-to-end. You build with Claude Code daily, critically review AI output, and augment your tools with context and constraints to make them effective. Product Instinct You don’t wait to be handed a roadmap. You identify which problems are worth solving, in what order, and why — and you make the case for building before anyone asks you to. What Would Set You Apart Background as a founding engineer or technical co-founder Experience in EdTech, regulated content, or domains where AI output quality has compliance implications Published thinking or external contributions in AI engineering — talks, writing, open source Practical experience with MCP (Model Context Protocol) or equivalent agent integration standards
