Machine Learning Engineer

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