Machine Learning Engineer

Jobtailor

Greater London

On-site

GBP 75,000 - 110,000

Full time

6 days ago

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Job summary

Jobtailor is seeking an ML Engineer to lead and scale a predictive modelling platform across multiple problems and tenants. You will design and iterate models including embedding-based encoders, temporal survival models, and gradient-boosted trees.

The role includes building robust evaluation pipelines, datasets, and model infrastructure for production use. You will push responsible AI practices, track frontier ML techniques, and collaborate with the Platform team to evolve the backend platform

Qualifications

Bachelor’s or Master’s degree in CS/ML or equivalent practical experience.
3+ years as ML Engineer or related role.
Strong Python and production experience.
Experience with ML pipelines using orchestration tools.
Hands-on with PyTorch, scikit-learn and gradient-boosting libs.
Experience deploying containerised systems and APIs.
Production experience with Kubernetes and cloud services.
PhD or advanced research with publications is a plus.

Responsibilities

Own and advance a predictive modelling platform across problem types and tenants.
Design, implement, and iterate models including embedding encoders and gradient-boosted trees.
Predict vulnerabilities in housing, health, and social domains.
Track ML developments and run structured experiments for production deployment.
Build evaluation pipelines, training datasets, and model infrastructure.
Support continuous improvement of NLP and predictive analytics.
Ensure responsible AI deployment with ethical and regulatory considerations.
Collaborate with Platform team on backend platform deployment and evolution.

Skills

Predictive Modeling

Machine Learning Engineering

Python Programming

Data Pipeline Development

Containerization with Kubernetes

Education

Bachelor’s Degree in Computer Science

Master’s Degree in Machine Learning

PhD in Computer Science

Tools

PyTorch

Scikit-learn

XGBoost

LightGBM

Dagster

Airflow

Prefect

Kubernetes

Azure Kubernetes Service

Hugging Face Transformers

Job description

Own and advance a predictive modelling platform that scales across problem types and tenants
Design, implement, and iterate models including embedding-based sequence encoders, temporal survival models, and gradient-boosted decision trees
Predict key vulnerabilities in housing, health, and other social domains
Track developments in ML and frontier models and run structured experiments to bring promising techniques safely into production
Build robust evaluation pipelines, training datasets, and model infrastructure
Support continuous improvement of natural language and predictive analytics
Ensure responsible AI deployment by embedding ethical and regulatory considerations throughout development
Work in the Platform team responsible for deployment and evolution of the backend platform underpinning Xantura’s core business

Requirements

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, Airflow, or Prefect
Hands-on experience with PyTorch, scikit-learn, and gradient-boosting libraries such as XGBoost or LightGBM
Practical experience defining and deploying containerised systems
Experience implementing APIs for internal services, such as FastAPI
Experience deploying containerised systems to production, particularly via Kubernetes
PhD in Computer Science, Machine Learning, or a related field with a strong publication record in text analytics, representation learning, or applied predictive modelling would be an advantage
Practical experience productionising LLMs would be an advantage
Experience with vector databases and retrieval-augmented generation (RAG) pipelines would be advantageous
Experience finding and productionising recent AI models via Hugging Face Transformers or OpenAI APIs would be advantageous
Experience building agentic systems via LangChain, AutoGen, or PydanticAI would be advantageous
Evidence of participating in Open-Source Software development, public hackathons, or other sharable coding samples would be an advantage
Deep expertise in embedding-based architectures, including bi-encoders and cross-encoders, for long-horizon text or temporal prediction tasks would be an advantage
Practical experience building and serving production-ready asynchronous APIs for embedding or other compute-intensive services would be an advantage
Proficiency in Python for high-performance data and model pipelines, with software engineering discipline including testing, versioning, and CI/CD
Good familiarity with the Azure ecosystem, including Azure Kubernetes Service, Azure Batch, Azure AI Foundry, Azure Machine Learning, Azure Blob Storage, and Azure Key Vault, would be an advantage

Core Competencies

Demonstrates expertise in predictive modeling, machine learning, and data pipeline development, with a strong focus on ethical AI deployment and continuous improvement of analytics. Proficient in Python and experienced in building scalable systems using modern orchestration and containerization tools.

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