Machine Learning Engineer

Alexander Daniels Global; Oxford; On-site; GBP 50,000 - 70,000; Full time

Job summary

A leading recruitment firm in Oxford seeks a Machine Learning Engineer to design and develop innovative machine learning solutions for optimizing manufacturing processes. The role involves collaboration with engineers and domain experts, alongside building robust MLOps pipelines. A Master’s degree in a relevant field and experience in probabilistic and Bayesian modelling are essential. Candidates should also have proficiency in programming and familiarity with cloud platforms.

Qualifications

• Master’s degree in a relevant field.
• Strong understanding of probabilistic model development.
• Experience with Bayesian modelling.

Responsibilities

• Design, develop, and validate machine learning models.
• Collaborate with engineers and scientists to engineer features.
• Build and maintain MLOps pipelines for automated model development.

Skills

Probabilistic model development

Bayesian modelling

Object-oriented programming

Software design principles

Cloud platforms (Azure, AWS, GCP)

Infrastructure-as-code tools (Terraform)

Education

Master’s degree in Machine Learning, Mathematics, or Statistics

Tools

Airflow

MLflow

Job description

Machine Learning Engineer - Onsite in Oxford

Employment Type: Full-time, Onsite

Are you passionate about applying cutting‑edge machine learning to real‑world challenges? This is an opportunity to work at the intersection of AI and advanced manufacturing, helping to optimize processes and material composition through innovative solutions.

What You'll Do

• Design, develop, and validate novel machine learning models to optimize manufacturing processes and material composition.
• Collaborate closely with process engineers, material scientists, and domain experts to identify and engineer meaningful features.
• Develop internal machine learning platforms to enable adoption and application of validated models.
• Work as part of a fast‑paced, agile development team, identifying and prioritizing opportunities to deliver new capabilities.
• Build and maintain robust MLOps pipelines for scalable, reproducible, and automated model development, deployment, and monitoring.
• Leverage tools such as Airflow for workflow orchestration and MLflow for experiment tracking, model registry, and lifecycle management, ensuring strong CI/CD practices and model governance.

Essential Skills

• Master’s degree in Machine Learning, Mathematics, or Statistics.
• Strong understanding of probabilistic model development.
• Experience with Bayesian modelling.
• Solid grasp of software design principles and best practices.
• Proficiency in at least one object‑oriented programming language.
• Familiarity with cloud platforms (Azure, AWS, GCP) and infrastructure‑as‑code tools (e.g., Terraform).