Machine Learning Engineer

Department: Platform Delivery Location: London Compensation: £50,000 - £70,000 / year Description In this role you will work in the Platform team – a function for the deployment and evolution of the backend platform that underpins the core of the Xantura business. Key Responsibilities * Own and advance a predictive modelling platform that scales across problem types and tenants, using it to design, implement, and iterate models (embedding-based sequence encoders, temporal survival models, gradient-boosted decision trees) that predict key vulnerabilities in housing, health, and other social domains. * Track developments in ML and frontier models, running structured experiments to bring promising techniques into production safely. * Build robust evaluation pipelines, training datasets, and model infrastructure to support continuous improvement of natural language \& predictive analytics. * Ensure responsible AI deployment, embedding ethical and regulatory considerations into every stage of development. Skills, Knowledge \& Expertise * Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related technical field – or equivalent practical experience. * 3+ years of professional experience as an ML Engineer, or related role. * Strong programming skills and production experience in Python. * Experience building and maintaining data or ML pipelines with an orchestration tool such as Dagster (or Airflow, Prefect, etc.). * Hands-on experience with common ML libraries and frameworks, e.g. PyTorch, scikit-learn, and gradient-boosting libraries such as XGBoost or LightGBM. * Clear evidence of practical experience defining and deploying containerised systems, i.e.: + Implementing APIs for internal services, e.g. via FastAPI; + Deploying containerised systems to production, in particular via Kubernetes. In addition, the following would be an advantage: * PhD in Computer Science, Machine Learning, or a related field with a strong publication record in text analytics, representation learning, or applied predictive modelling. * Practical experience productionising LLMs, i.e.: + Working with vector databases and developing retrieval-augmented generation (RAG) pipelines – experience setting up/configuring vector DBs, as well as using, would be advantageous; + Finding and productionising recent AI models (e.g. via Huggingface (transformers), OpenAI APIs); + Building agentic systems (e.g. via LangChain, AutoGen, PydanticAI). * Evidence of participating in Open-Source Software (OSS) development, public hackathons, or other sharable coding samples. * Deep expertise in embedding-based architectures, including bi-encoders, cross-encoders, etc. for long-horizon text or temporal prediction tasks. * Practical experience building and serving production-ready, asynchronous APIs for embedding and/or other compute-intensive services. * Proficiency in Python for building high-performance data and model pipelines, with strong software engineering discipline (testing, versioning, CI/CD). * Good familiarity with the Azure ecosystem (Azure Kubernetes Service, Azure Batch, Azure AI Foundry, Azure Machine Learning, Azure Blob Storage, Azure Key Vault) . This is a Hybrid opportunity with the expectations of being in the office 1 - 2 days a week. Job Benefits * Competitive salary reviewed annually * Work for a passionate, mission-driven company solving society’s big problems * Work flexible hours around life commitments with a focus on delivering company value rather than hours worked * Training and development opportunities * 25 days annual leave (plus bank holidays) * Company pension * Private medical insurance * Generous enhanced parental leave policies * Cycle to work scheme * Flu Vaccinations, * Eye Test and contribution towards Glasses for VDU use * Employee Assistance Programme + Mental health and wellbeing support + Remote GP access + Counselling/therapy + Physiotherapy + Medical second opinions