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Taken from the listing. Check the full advert before you apply.
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.
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.
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.
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.
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’t have Typescript, you will need to learn): React/Next.js (App Router, RSC, server actions), tRPC/GraphQL, Node/Express.
PostgreSQL at scale via an ORM, with migration discipline on large, live schemas.
Event-driven systems on a message bus (we use RabbitMQ) — versioned events, idempotency, backward-compatible migration.
A high engineering maturity bar as second nature: typing, testing, feature flags, ADRs, CI/CD, observability.
AI-native delivery in practice — you build daily with Claude Code, Cursor, or the current best tool, and understand context management, model selection, tool design, and the token economics that separate a prototype from an affordable production system.
GTM/acquisition-funnel experience — you've owned surfaces where onboarding and eligibility meet real customers.
Strangler-pattern pragmatism — legacy monolith and greenfield edge-native product, at the same time.
Accessibility and UX as defaults on learner-facing surfaces, not afterthoughts.
This role won't fit if you'd rather run ceremonies than stay in the system, the design, and the code yourself. It also won't fit if you see AI tooling as a productivity tweak rather than a step change in how a team works. And it needs someone who renegotiates the scope out loud when it's wrong, not someone who absorbs it and hopes it works out.
TypeScript core (React/Next.js, Node) with PostgreSQL and RabbitMQ as the event backbone, plus Python and Elixir services — from RAG pipelines to a Claude-powered edge-native product. GitHub, CircleCI, Datadog. AI-assisted development is first-class, not bolted on.
You'll lead full-stack engineers (with Python/Elixir specialists), report to the Senior Director of Engineering for Diagnose & Prescribe, and calibrate regularly with a peer group of EMs — you're not doing this alone. We reject micro-management: we set the objective, you and your team own the how. Kindness, candour, and curiosity over hierarchy — bad news travels as fast as good.
Benefits
Time off: 27 days holiday (including 3 winter shutdown days) and bank holidays, plus 5 extra days off (1 life event, 2 volunteer days and 2 summer shutdown days).
Health and wellbeing: Bupa private medical insurance, a medical cash back plan, therapy through Spill and gym access through Wellhub.
Equity for all: we believe that when Multiverse wins, we all win, which is why every permanent employee receives share options.
Financial security: life insurance at 3x salary, a workplace pension with 4% contribution from us, Cycle to Work and a Tech Scheme.
Family support: enhanced maternity and paternity leave, adoption and shared parental leave, paid leave after pregnancy loss, and a workplace nursery scheme.
Hybrid working: 3 days a week in the office - except for Coaches & Instructors who come into the office 1 day per month. Plus 10 days a year to work from anywhere.
Work perks: quarterly team socials, company-wide events, happy hours, lunch delivered to the office once a week, fully stocked snack bar and a drinks fridge!
Our Commitment to Diversity, Equity and Inclusion
We’re an equal opportunities employer. And proud of it. Every applicant and employee is afforded the same opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. This will never change. Read our Equality, Diversity & Inclusion policy here.
Our Commitment to Safeguarding
Multiverse is committed to safeguarding and promoting the welfare of our learners. We expect all employees to share this commitment and adhere to our Safeguarding Policy, our Prevent Policy and all other Multiverse company policies. Successful applicants will be required to undertake at least a Basic check via the Disclosure Barring Service (DBS).
For roles that will involve a Regulated Activity, successful applicants must also undergo an Enhanced DBS check, including a Children’s Barred List check and a Prohibition Order check. Roles involving Regulated Activity may interact with vulnerable groups, therefore are exempt from the Rehabilitation of Offenders Act 1974 meaning applicants are required to declare any convictions, cautions, reprimands, and final warnings.
Providing false information is an offence and could result in the application being rejected or summary dismissal if the applicant has been selected, and possible referral to the police and the DBS.
Sourced from the employer’s own listing · last verified 11 Nov 2026. Report this listing