aimlroles

Machine Learning Engineer

Xantura Open · verified Oct 22, 2026
Hybrid London Full-time Mid-level Machine Learning Engineering

At a glance

Mid-level Machine Learning Engineering role at Xantura. London · full-time · no salary stated · we estimate £60,000–£97,524 across the United Kingdom.

Estimated pay

Xantura has not published a salary for this role. The figure below is our estimate, not theirs.

£60,000 – £97,524
What the middle half of mid-level machine learning engineering roles in the United Kingdom paid, among postings that did state a salary.
Worked out from 5 postings that stated pay, median £86,000. Last updated Oct 22, 2026.
RoleMachine Learning Engineering
SeniorityMid-level
LocationLondon
WorkplaceHybrid
EmploymentFull-time
Role brief

aimlroles summary, based on the employer's posting.

What you'll do

  • Develop and refine a predictive modelling platform that serves different problem types and tenants
  • Build models to identify vulnerabilities across housing, health and other social areas
  • Test promising ML and frontier-model techniques, then move suitable approaches into production safely
  • Create evaluation workflows, training data and model infrastructure for ongoing language and predictive analytics improvements
  • Build ethical and regulatory checks into each stage of model development and deployment

What you bring

  • At least three years working as an ML engineer or in a related professional role
  • Strong Python skills, including experience using it in production
  • Experience maintaining data or ML workflows with an orchestration tool, plus hands-on use of common ML frameworks
  • Practical experience building internal APIs and deploying containerised systems, particularly with Kubernetes

Who this fits

This mid-level role suits an ML engineer who can develop predictive models and production infrastructure in Python. It is hybrid, with one to two office days each week in London.

From the employer


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.

  • 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.

  • 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.