AI Content Specialist - Adoption

The Opportunity

Adopt is Multiverse's newest bet: an AI adoption platform that helps every employee at our enterprise customers answer one question: "What do I actually do with AI in my job?" This is done through role-specific playbooks, use cases, and resources, generated and surfaced at the moment of need. Adopt runs as a startup within the business: a small, fast-moving team leveraging Multiverse's customer relationships, IP, and reputation, but building and shipping like a Series A company. We’re moving from a hands‑on, manually curated content process to one where AI generates the vast majority of content at a scale no human team could match. We’re aiming to support large enterprise deals and thousands of learners and use cases by the end of the year, which means the systems and the people who guard their quality have to be excellent.

This is genuinely a build‑it‑as‑you‑go role on a product that’s still finding its shape. You’ll have real influence over how a brand‑new content quality function works, not a long‑established process to maintain.

The Role

Adopt uses generative AI throughout the content development process. The AI Content Specialist is the human in the loop who ensures that content is high‑quality and trustworthy. You will own the quality and currency of the use case library; reviewing what’s created, identifying where it falls short, keeping content accurate as AI tools change, and curating the knowledge bases that our AI models draw on.

This is a role for someone who is energised by working with AI‑generated content at scale, has the instincts of a strong content developer, and who cares more about whether content works than about authoring it from scratch. Your success is measured by the accuracy, relevance, and real‑world effectiveness of the content employees act on, not by the volume produced. This is an individual contributor role, reporting to the Director of Learning for Adopt.

What your first month looks like

You’ll spend your first few weeks getting hands‑on with the live use case library and our QA process: sampling generated content, learning how to use our AI tooling, and understanding how feedback currently flows back into the generation system. Within your first month, you should be independently reviewing your share of AI‑generated content we quality‑check, and starting to spot the first patterns worth feeding back to the team building the generation system.

Key Responsibilities

• Quality‑assure AI‑generated content: Review a sample of the use cases our AI content generation system produces for accuracy, working prompts, realistic outputs, and credible time savings. You are the human judgment layer on top of automated and LLM‑as‑judge checks, catching what they miss and holding the quality bar in practice.
• Improve the system, not just the output: Identify patterns in where generation falls short and feed those insights back so the library gets better over time, not just bigger. You will work closely with the AI Content Systems Architect, who owns the generation system; you hold the line on quality day to day and tell them what needs to change.
• Keep content current: AI tools change constantly, and content that contradicts a user's lived experience of a tool erodes trust faster than no content at all. You will use an AI‑assisted process to identify use cases affected by tool changes and update them, keeping the library free of stale or inaccurate guidance.
• Curate the knowledge base: Maintain the generic and customer‑specific knowledge bases of best practices and AI tool documentation, deciding what is accurate, current, and worth including. Quality‑check uploaded learning resources and verify that tagging and mapping to use cases are correct.
• Support governance and feedback: Triage user feedback on content, route it appropriately and act on it. Support reviewers (customer‑side content owners) on quality standards where needed.

Success Metrics

• Content accuracy and quality: Generated and curated content meets the quality bar, evidenced through sampling, LLM‑as‑judge evaluation, and a low rate of user‑reported issues.
• Content effectiveness: Content drives real‑world application. Use cases that are launched, completed, and generate impact stories.
• Content currency: The library stays current as AI tools change; stale or inaccurate use cases are identified and resolved promptly, with minimal trust‑eroding errors reaching users.
• System improvement: Insights from QA demonstrably improve content generation quality over time, measured by quality trends across the sampled set.
• Knowledge base health: The generic knowledge base and resource library are accurate, current, and well‑tagged, supporting high‑quality generation and surfacing.

Skills & Qualifications

• Editorial judgement and quality instinct (required). You can look at a piece of content and quickly judge whether it is accurate, useful, and meets the bar and articulate why. You have strong attention to detail and don’t settle for mediocre content. This is the core of the role.
• Hands‑on AI building skills (required). These are some examples of what we are looking for: You can use an AI coding tool (e.g. Claude, Cursor) to build a working front end or simple agent end‑to‑end, without engineering support. It doesn't need to be production code, but it needs to actually run. You're comfortable enough with GitHub to navigate a repo, create a branch, submit a pull request, etc. You can point to a small personal portfolio of things you've actually built this way, like side projects, internal tools, automations, and talk through what they do, what you used, and where you got stuck.
• Evals fluency (required). You understand structured AI‑output evaluation: building a labelled example set, running a model or prompt against it, scoring outputs against a target, and using that signal to improve a prompt or system. You don't need to have built an eval pipeline from scratch, but you should be able to design a simple rubric and explain how you'd use it to catch quality problems at scale.
• A bias toward effectiveness over volume. You're an advocate of applied learning and believe content only matters if it changes what someone does at work. You care most about what works and doing more of that.
• Rigour and data fluency. You can work with various sources of data to track quality trends, identify insights, and prioritise where to focus.
• Bias to action and comfort with ambiguity. This is an early‑stage, "Beta" product operating as a startup within the business. You're comfortable launching imperfect things, right‑sizing effort to impact, and adapting as the role and platform evolve.

Bonus experience

• Experience in learning design, instructional design, content strategy, or technical writing/editing.
• Experience designing for or working in technical or AI disciplines.
• Prior exposure to LLM evaluation tooling or platforms (e.g. LLM‑as‑judge frameworks, offline/online eval tooling) beyond the foundational fluency described above.

Why Join Now

Adopt is early. You'll be one of the first people to define what "content quality" means for an AI‑generated content system at this scale, not inheriting someone else's playbook. As the function matures, this role is a natural path toward owning quality and evaluation strategy more broadly across Adopt's content systems as the team and product grow.

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.

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.