Senior Machine Learning Engineer

Senior Machine Learning/AI Engineer

• £100,000-£125,000
• Hybrid working model: three days per week in the office (Monday, Tuesday, plus one flexible day) LDN
• 27 days of annual leave plus 8 bank holidays and 5 additional days, including life event, volunteering, and company-wide wellbeing days.
• Private medical insurance, medical cashback, life insurance, gym membership, wellbeing resources, and access to mental health support.
• Opportunity to work remotely from anywhere for up to 10 days per year.

We are currently partnered with a company that believes technology should empower everyone to achieve their potential.

As an Senior Machine Learning/Applied AI Engineer, the successful candidate will turn cutting-edge AI research into real-world products that make learning and development smarter, more personalised, and more impactful for thousands of users.

The Senior Machine Learning/Applied AI Engineer role sits within a growing Applied Science team and is focused on building, deploying, and scaling AI systems that operate in real production environments. This is a senior, hands-on role for engineers who have moved beyond experimentation and have delivered LLM-powered systems that directly impact products, users, or business outcomes.

Key Responsibilities

• Design & Deliver AI Solutions: Partner with Product, Design, and Data teams to shape and deliver AI-powered features that drive meaningful impact for learners and create value for customers.
• Leverage Large Language Models (LLMs): Design, fine-tune, and integrate LLM-powered solutions for use cases including content generation, semantic search, summarisation, and personalised learning experiences.
• Build & Integrate Models: Develop, fine-tune, and embed machine learning models into production systems using modern AI tooling, ensuring solutions are scalable, reliable, and performant.
• Own the End-to-End Lifecycle: Take responsibility for the full lifecycle, from raw data and experimentation through deployment, monitoring, and continuous iteration.
• Measure What Matters: Track performance, accuracy, and adoption of AI features, using insights to drive continuous improvement.
• Enable Others: Share expertise and help make AI approachable, enabling teams across the organisation to enhance their work with AI.
• Lead in MLOps & Cloud Infrastructure: Build robust pipelines for model training, deployment, monitoring, and retraining using AWS and modern MLOps best practices.
• Champion Innovation: Stay ahead of emerging AI tools and techniques, applying them to deliver exceptional, user-focused experiences.

What the Company Is Looking For

• Hands-On AI/ML Expertise: Experience building and deploying machine learning models using frameworks such as PyTorch, TensorFlow, or scikit-learn.
• LLM Experience: Proven experience working with large language models (e.g. GPT, Claude, Gemini) in production environments, including prompt engineering, evaluation, and safety considerations.
• Strong Engineering Skills: Proficiency in Python and TypeScript, with experience building APIs, microservices, and cloud-native applications.
• Modern AI Tooling: Familiarity with emerging AI development tools such as Cursor and Gemini is highly desirable.
• Cloud & MLOps: Practical experience deploying AI solutions on AWS, including CI/CD, model versioning, observability, and retraining pipelines.
• Data Fluency: Skilled in working with structured and unstructured data, including preprocessing and feature engineering.
• User-Centred Mindset: Ability to translate complex AI capabilities into intuitive and seamless product experiences.
• Collaborative Approach: Thrives in cross-functional, creative teams and values building together.
• Growth Mindset: Curious, open to feedback, and committed to contributing to an inclusive, high-performing culture.

Click apply to be considered for shortlisting.