Applied AI Engineer at Multiverse

London, England, United Kingdom

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

EdTech, TechnologyIndustries

Requirements

Hands-On AI/ML Expertise: Built and deployed machine learning models using frameworks like PyTorch, TensorFlow, or scikit-learn
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 CI/CD for models, version control, observability, and retraining pipelines
Data Skills: Skilled at working with structured and unstructured data, applying preprocessing and feature engineering techniques
User Focus: Ability to translate complex AI capabilities into product experiences that feel effortless and intuitive
Collaborative Approach: Works best in creative and cross-functional teams and thrives when building together
Growth Mindset: Curious, open to feedback, and excited to share knowledge

Responsibilities

Design & Deliver AI Solutions: Partner with Product, Design, and Data teams to shape and deliver AI-powered features that generate real impact for learners, value for 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 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: Explore new AI tools, including Cursor and Gemini, and apply them to create exceptional user experiences

Skills

Key technologies and capabilities for this role

LLMsFine-tuningMachine LearningSemantic SearchContent GenerationSummarisationPersonalised LearningCursorGeminiAI Solutions

Questions & Answers

Common questions about this position

Is this role remote or hybrid?

The position is hybrid.

What salary or compensation does this role offer?

This information is not specified in the job description.

What skills are required for the Applied AI Engineer role?

Candidates need hands-on AI/ML expertise with frameworks like PyTorch, TensorFlow, or scikit-learn, and experience working with large language models (LLMs). Additional skills include designing and integrating LLM-powered solutions, building MLOps pipelines with AWS, and using tools like Cursor and Gemini.

What is the company culture like at Multiverse?

Multiverse focuses on empowering everyone to achieve their potential through technology, with a mission to equip the workforce for the AI era. The culture emphasizes collaboration across Product, Design, and Data teams, sharing expertise, and championing innovation.

What makes a strong candidate for this Applied AI Engineer position?

Strong candidates have hands-on experience building and deploying ML models with PyTorch, TensorFlow, or scikit-learn, expertise in LLMs, and skills in MLOps, AWS, and tools like Cursor and Gemini. They should be able to own the end-to-end AI lifecycle and collaborate across teams to deliver impactful solutions.

Multiverse

Provides professional apprenticeships for workforce development

About Multiverse

Multiverse focuses on workforce development through professional apprenticeships, providing on-the-job training programs that help individuals gain skills for high-paying jobs and career advancement. The company operates in the United States and the United Kingdom, partnering with various organizations to tailor apprenticeship programs to meet specific employer and individual needs. Unlike traditional hiring methods, Multiverse's approach is designed to be equitable and accessible, allowing diverse talent to succeed. The company also offers ongoing support and mentorship to apprentices, ensuring they can navigate their career paths effectively. Multiverse's goal is to create a diverse group of future leaders by transforming the way talent is developed and nurtured.

London, United KingdomHeadquarters

2016Year Founded

$402.7MTotal Funding

SERIES_DCompany Stage

Social Impact, AI & Machine Learning, EducationIndustries

1,001-5,000Employees

Risks

Competition from universities offering flexible, career-focused programs may challenge Multiverse.

Rapid AI development could outpace Multiverse's training program updates.

Economic downturns may reduce corporate spending on training, impacting revenue.

Differentiation

Multiverse offers AI-powered apprenticeships, a unique alternative to traditional university education.

The company partners with diverse industries, enhancing workforce skills through tailored programs.

Multiverse's acquisition of Searchlight boosts its AI capabilities and competitive edge.

Upsides

Partnership with Microsoft aims to skill 1 million people in AI by 2025.

Recent leadership appointments focus on financial growth and curriculum development.

AI-Powered Productivity apprenticeships are accredited and eligible for public funding in the UK.

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