Staff AI Engineer

About the company

Multiverse is an edtech company founded in 2016 that started with digital apprenticeships and now provides enterprise AI training and reskilling.

About this 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 ROLE 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. Multiverse is in a uniquely strong position to do that, and getting it right has implications beyond the company: for the UK tech sector and the broader economy. The AI Transformation team exists to make that real, starting with Multiverse itself. This is not a team that bolts AI onto the edges of the business or ships a handful of internal productivity tools. The mandate is bigger: to rebuild how the company actually works, function by function, and to establish the engineering practices that make Multiverse an AI-first company from the core out. That work matters twice over. Get it right inside Multiverse and we move faster, serve learners better, and operate at a level few organisations can match. But Multiverse also exists to build the workforce that every other company is reaching for. The way we transform ourselves becomes the standard we set for everyone else. You are not just changing one company, you are building the blueprint others will follow. The team is one small, focused squad, accountable for outcomes end to end. You work closely with the wider engineering org building Multiverse's customer-facing product, and alongside the teams whose work you are helping to reinvent. The structure is flat and fast. No shared queues, no bureaucratic overhead between having an idea and shipping it. Whilst we are building something entirely new, Multiverse has an established product, existing infrastructure, and engineering teams in London and Berlin. You need to be as comfortable integrating existing systems and working across team boundaries as you are building new ones from scratch.   WHAT YOU WILL DO Own the architecture of our internal agentic operating system. The team's work spans the full surface of how Multiverse operates. You own the technical architecture of our agentic operating system: the agent orchestration, context strategy, tool integrations, evaluation framework, and production operation. Your design decisions shape what is possible for human and AI teams at Multiverse Ship production AI agent systems. This is a building role. You write code, review code, and own the quality of what goes to production. You will personally build and deliver significant agent systems. On a squad this size, nobody leads from a whiteboard. Design multi-agent coordination. Task decomposition across agents, handoff protocols, shared state management, orchestration logic. You know the difference between agents that genuinely coordinate and agents that run sequentially and hope for the best. You design the patterns that make multi-agent systems reliable. Build the evaluation and quality infrastructure. Automated eval pipelines, human-in-the-loop review systems, regression testing for prompt changes, domain-specific quality metrics. You treat evaluation as a first-class engineering concern and build the systems that make it possible at scale. Drive cost engineering. Token economics, caching strategies, model routing, prompt optimisation. The cost profile of production AI systems requires active engineering attention, and you build the cost awareness and tooling into the architecture rather than bolting it on later. Build the integration layer that makes existing Multiverse systems agent-accessible. APIs, MCPs, shared data contracts, and the tooling that connects agents to the platform, content systems, and the tools the company runs on. This means building real working relationships with engineering teams across London and designing interfaces that serve both sides well. Set the standard. You define patterns for prompt management, retrieval, guardrails, and testing that the wider team and eventually the whole organisation adopts — and that, in time, shape how the companies who learn from Multiverse do this too. You do this through code, documentation, and architectural decisions, not through mandates. Mentor the team. Code review, architectural guidance, pairing on the hardest problems. You are not a line manager, but your technical leadership directly shapes the growth of the engineers around you.   WHAT WE ARE LOOKING FOR Production AI Agent Engineering You have shipped multi-agent systems or complex AI products to real users. You understand the engineering challenges that make agent systems a distinct discipline: - Context management. Designing what enters the context window and what stays out. Retrieval strategies, chunking, conversation memory, summarisation, and the cost/quality trade-offs of each. You have made these decisions in production and seen the consequences. - Model selection and routing. Choosing the right model for each task based on capability, latency, cost, and reliability. Building routing logic that matches work to the appropriate model rather tha