Xantura
Machine Learning Engineer
Greater London
£50k – £70k/yr
Posted 1 day ago
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Machine Learning Engineer
Department: Platform Delivery
Employment Type: Permanent - Full Time
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.
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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).
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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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Skills
Python
Machine learning
Predictive modelling
Data pipelines
ML pipelines
Dagster
PyTorch
Scikit-learn
XGBoost
LightGBM
FastAPI
Kubernetes
Containerisation
Natural language processing
Cloud computing
Software engineering