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Multiverse

Senior AI Engineer

LondonHybrid · United Kingdom
Senior · FullTime · Engineering
POSTED 29 SEPTVERIFIED 14H AGO
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Role
41 days opentrue age
Posted 29 Sept
Pay
£89k–£111kno pay posted
Market p25–p75
£89k–£111k
London · n=6 · 365d to 6 Nov
CompanyMultiverse →
27open roles
Opened · 30d
+0
Closed · 30d
−1
3d of snapshots · 30d window
Market
1,501open · Senior ML / AI
Opened · 7d
+987% of open
All ml / ai
6,615open
All tracked boards · live 10 Nov · n=1,501
Visa
✓licensed sponsor
UK licensed sponsor
✓Worker (A rating)
Checked 9 Nov
LLMs

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.

The AI Transformation team

AI Transformation is a small, focused squad that is driving Multiverse’s next wave of growth. Our mandate is to rebuild how the company actually works, function by function, and to establish the practices that make Multiverse an AI-first company from the core out.

But our work doesn’t stop there. We are building a product that we will take to market to truly be a transformation partner to our customers. Joining this team means shaping the future of Multiverse, of our customers, and of the wider workforce as we navigate the new world of work.

The team is high velocity, inherently cross-functional and broad in scope. We work alongside colleagues whose work we are reimagining, and closely with the wider engineering org building Multiverse's current customer-facing product. The structure is flat and fast, with egos left at the door, bias to action and fun at our core.

What your day-to-day will look like

Every day will be different, but you can expect it to include:

Own and deliver end-to-end AI systems: agents and the platforms they depend on. You identify a user problem, break it down and build the AI system that solves it, in close partnership with the teams who will adopt it

Design context and retrieval strategies: the retrieval pipelines, conversation memory, summarisation strategies, chunking logic and RBAC that make context useful and permission-aware

Build evaluation frameworks. You build automated eval pipelines and human-in-the-loop review processes that tell the team whether its AI systems are doing what they should.

Design tool integrations. Agents are only as capable as the systems and context they can reach. You design and build the tool layer: MCPs, APIs, data contracts, and the error handling that makes tool use reliable.

Influence technical direction. You have opinions about how things should be built, and you back them up with evidence. You contribute to architectural decisions, push back when the team is heading in the wrong direction, and propose better approaches.

What We Are Looking For

Production AI Agent Engineering: You have shipped AI systems that serve real users at scale. You understand the engineering challenges that make agent systems different from conventional software, including context management, model selection and routing, cost engineering, tool use and agent augmentation, and evaluation

Product mindset: Your natural instinct is to identify the real user pain, understand their requirements and build pragmatic, high-impact solutions. You test, learn and iterate fast, and are comfortable executing under high ambiguity

Full-Stack Delivery: You work across the stack: LLM integration, backend services, data pipelines, and enough frontend to ship end to end

Communication: You can explain technical concepts clearly to both engineers and business colleagues. You proactively engage the people around your work, and bring them along with you on the journey

Source · MultiverseFirst seen 2 Nov1 editsOriginal ↗