Ready to shape the future of data?
Matillion is the intelligent data integration platform.
We're changing how the world works with data – and we need driven, curious people who think big and move fast.
We built the Data Productivity Cloud to supercharge data productivity, and now we’re shaping the future of data engineering with Maia – our AI-powered virtual data engineers that help teams design, build, and manage data pipelines at unmatched speed.
Join #TeamGreen, where the mission comes first, collaboration drives us forward, and everyone pulls in the same direction to make a dent in the universe bigger than ourselves.
We're hiring a Software Engineer to drive our GenAI initiatives. The ideal candidate brings strong Java proficiency and a deep interest in Generative AI technology.
About the Role
At Matillion, we’re on a mission to eliminate friction in data engineering and supercharge productivity through the power of AI. As part of the Product organisation, our Data Science team is laser-focused on two things: pioneering how artificial intelligence and machine learning are woven into our products, and understanding how our customers use those products to drive continuous innovation.
This Software Engineer role sits at the heart of that mission. You’ll be hands-on in developing, optimising, and experimenting with advanced models—helping to shape how Maia and our AI systems evolve end-to-end. From reinforcement learning to context and memory, from structured knowledge to performance optimisation, you’ll explore new approaches and translate them into product-ready features that deliver real value to our users.
If you're driven by impact, innovation, and the chance to work at the frontier of AI in a product-led organisation, this is the opportunity for you.
We value in-person collaboration here at Matillion, therefore this role will ideally follow our hybrid work structure where employees work 2 days a week in the Manchester office. However, we can be flexible for the right person.
What you will be doing
Train, fine-tune, and evaluate large-language / foundation models, while also applying parameter-efficient techniques, reinforcement learning, and prompt engineering approaches.Build and enhance memory and context management systems: manage short- and long-term context, embeddings, and retrieval pipelines so Maia can access relevant information effectively.Integrate structured knowledge sources such as knowledge graphs and ontologies to improve consistency, reasoning, and reduce hallucinations.Develop capabilities for ingesting and processing multiple unstructured and multi-format data types (text, audio, etc.), designing pipelines for parsing, embedding, indexing, and retrieval.Optimise inference and system performance: reduce latency, increase throughput, design efficient prompt pipelines, and engineer for cost-effective scaling.Define and implement monitoring and evaluation frameworks: track metrics such as accuracy, drift, error rates, and user satisfaction; set up feedback loops to ensure robustness and reliability.Collaborate with engineers, product managers, and UX teams to turn experimental techniques into stable, scalable features that deliver measurable value.Stay hands-on: write code, prototype solutions, and contribute directly to implementation (with some work in Java where relevant).
What we are looking for
Deep AI Expertise: Proven experience working with large language / foundation models, including training, tuning, and applying modern GenAI techniques such as reinforcement learning, memory and context systems, retrieval augmentation, structured knowledge integration, optimisation, and evaluation.Academic Rigor: Master’s degree in a relevant field required; PhD preferred.Programming Ability: Strong coding skills in Java are required.Product Delivery Experience: Track record of delivering software features or products in a collaborative environment, ideally within a product-led organisation.Curiosity & Drive: Passion for emerging AI technologies, with the ability to critically evaluate new research and translate it into practice.Collaborative Mindset: Comfortable working cross-functionally to move from prototypes to production-ready features.
