AI Staff 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 an AI Solutions Engineer on our Learning team, you’ll transform cutting-edge AI research into real-world tools that make Multiverse’s learning content smarter, more personalised, and truly impactful for thousands of users. Join us to architect and lead the development of tooling that enables our Learning team to create world class experience building upon the AI tooling we’ve developed over the last year and evolve it to new capabilities and potential.
Key Responsibilities
Design, Architect & 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.
Establish LLM Best Practices: Define the technical standards and governance for leveraging Large Language Models (LLMs), including design, fine-tuning, and integration for high-impact production use cases 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.
Mentor and Scale Expertise: Mentor and coach engineers across teams, sharing your deep expertise to make AI approachable and set the direction for best practices, significantly elevating the team's capabilities.
Lead in MLOps & Cloud Infrastructure: Build robust pipelines for 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.
Drive Organisational Adoption: Champion new technologies and approaches (including AI-assisted tools), driving their successful adoption across multiple teams while balancing experimentation with pragmatic delivery.
Cross-Team Influence: Act as a key technical advisor and connector across product, design, and engineering, ensuring alignment on strategic initiatives.
About you
Proven Staff-Level experience: 6-7 years in software engineering with strong understanding of Applied AI fundamentals Experience working in cross-functional product teams
LLM Expertise: Experienced in working with large language models (e.g., GPT, Claude, Gemini Pro) for production use cases, including prompt engineering, evaluation, and safety & inclusivity considerations.
Strong Engineering Skills: Proficient in Python and TypeScript, with experience building APIs, microservices, and cloud-native applications.
Experience with AI Tools: Familiarity with emerging AI tooling platforms such as Cursor and Gemini is highly desirable.
Cloud & MLOps: Practical experience deploying AI solutions on AWS, with a strong grasp of version control, observability, and evaluation pipelines.
Data Skills: Skilled at working with structured and unstructured data, applying preprocessing and feature engineering techniques.
User Focus: You can translate complex AI capabilities into product experiences that feel effortless and intuitive.
Collaborative Approach: You work best in creative and cross-functional teams and thrive when building together.
Growth Mindset: You’re curious, open to feedback, and excited to share what you learn while contributing to an inclusive, high-performing culture.
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.