Applied AI Engineer at Multiverse
London, England, United Kingdom -
Full Time


Start Date

Immediate

Expiry Date

03 Dec, 25

Salary

0.0

Posted On

03 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

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 June 2022, we announced a $220 million Series D funding round co-led by StepStone Group, Lightspeed Venture Partners and General Catalyst. With a post-money valuation of $1.7bn, the round makes us the UK’s first EdTech 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.

Responsibilities
  • Design & Deliver AI Solutions: Partner with Product, Design, and Data teams to shape and deliver AI-powered features that generate real impact to our learners, value for our customers, and align with Multiverse’s mission.
  • Leverage Large Language Models (LLMs): Design, fine-tune, and integrate LLM-powered solutions for tasks such as content generation, semantic search, summarisation, and personalised learning experiences.
  • Build & Integrate Models: Develop, fine-tune, and embed machine learning models into production systems using tools like Cursor and Gemini, ensuring they are fast, scalable, and dependable.
  • Own the End-to-End Lifecycle: Take responsibility for the journey from raw data through experimentation, deployment to users, and continuous iteration.
  • Measure What Matters: Track the performance, accuracy, and adoption of AI features, and use those insights to drive constant improvement.
  • Enable Others: Share your expertise and make AI approachable, helping colleagues across teams see how it can enhance their work.
  • Lead in MLOps & Cloud Infrastructure: Build robust pipelines for model training, deployment, and monitoring using AWS cloud services and modern MLOps best practices.
  • Champion Innovation: Keep us ahead of the curve by exploring new AI tools, including Cursor and Gemini, and applying them to create exceptional user experiences.
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