Applied AI Engineer

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.

At Multiverse, we believe technology should empower everyone to achieve their potential. As a Senior member of the Applied AI team, you will sit at the intersection of data science and software engineering. You won’t just build models in isolation; you will transform cutting-edge AI research into scalable, real-world products that make learning and development smarter and more personalised for thousands of users. You will play a pivotal role in steering our AI roadmap, guiding collaborative efforts across product and engineering to shape the future of work and education.

• Design & Deliver AI/ML solutions: Translate complex stakeholder queries and business hypotheses into actionable experiments and AI/ML model requirements. Partner with Product and Design to deliver features that align with Multiverse’s mission.

• Architect LLM & Agentic Workflows: Design and integrate LLM-powered solutions (e.g., GPT, Claude, Gemini) for content generation and personalized learning. Build out our Knowledge Graph capability to underpin agentic workflows and semantic search.

• Own the End-to-End Lifecycle: Take full ownership of the journey from data lineage and preprocessing through to experimentation, deployment, evaluation and continuous iteration. Ensure all models adhere to software engineering best practices for production-grade reliability.

• Experimentation Rigour & Quality: Proactively monitor and refine models to optimise effectiveness while minimising sampling/analytical biases and operational challenges. Build robust evaluation frameworks to measure accuracy, safety, and inclusivity.

• Lead in MLOps & Infrastructure: Build and maintain scalable pipelines for model training and deployment using AWS and modern MLOps practices. Leverage AI-assisted tools like Cursor and Gemini to accelerate development velocity.

• Strategic Influence & Mentorship: Bridge the gap between technical concepts and business objectives by communicating actionable insights to stakeholders. Share your expertise to make AI approachable, helping colleagues across Multiverse see how it can enhance their work.

Technical Mastery

• Experience: 5+ years of Data Science, Machine Learning, or AI Engineering experience, with a proven track record of leading complex AI/ML projects from concept to production.

• ML & LLM Stack: Proficient in Python and its ecosystem (e.g. NumPy, Pandas, Scikit-Learn, PyTorch). Deep experience with LLM orchestration (e.g. Langchain) and prompt engineering.

• Data Engineering: Advanced knowledge of SQL and experience working with both structured and unstructured data

• Software Rigor: Strong proficiency in building APIs and microservices. Comfortable with GitHub, CI/CD, observability & evaluation practices, and Infrastructure as Code (e.g., Terraform).

• Cloud Native: Practical experience deploying and monitoring AI solutions within AWS, Azure or similar cloud environments.

Mindset & Approach

• Analytical Rigour: You have a tenacious and pragmatic approach to problem-solving, with exceptional attention to detail regarding data lineage and bias.

• User-Product first approach: You can translate complex AI capabilities into effortless and intuitive product experiences that drive value for our users.

• Growth Mindset: You are curious, open to feedback, and passionate about continuous learning in the fast-evolving AI landscape.

Nice to Have

• Familiarity with the education or skills sector.

• Experience with knowledge graphs and semantic web concepts.

Benefits

• Time off - 27 days holiday, plus 5 additional days off: 1 life event day, 2 volunteer days, 2 company-wide wellbeing days (M-Powered Weekend) and 8 bank holidays per year

• Health & Wellness- private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership & wellness resources through Wellhub and access to Spill - all in one mental health support

• Hybrid work offering - for most roles we collaborate in the office three days per week with the exception of Coaches and Instructors who collaborate in the office once a month

• Work-from-anywhere scheme - you'll have the opportunity to work from anywhere, up to 10 days per year

• Space to connect: Beyond the desk, we make time for weekly catch-ups, seasonal celebrations, and have a kitchen that’s always stocked!

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.