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.

Department: Platform Delivery

Location: London

Compensation: £50,000 – £70,000 / year

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

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