Machine Learning Engineer

Alloyed Limited; Yarnton; On-site; GBP 65,000 - 90,000; Full time

Job summary

Alloyed Limited in the UK is building the future of advanced metal components through ML-driven processes. We seek a driven machine learning specialist to design, develop and validate models that optimize manufacturing and material composition.

You will collaborate with process engineers, material scientists and data teams, and you will build robust MLOps pipelines using Airflow and MLflow to support scalable, reproducible model deployment and governance.

Qualifications

• Bachelor's degree in science, engineering, mathematics or computer science (2:1 minimum).
• Strong python development skills.
• Practical experience in the development of machine learning models and/or deep learning to solve complex science and engineering problems.
• Experience with MLOps tools and practices, including Airflow, MLflow, and containerization (e.g., Docker).
• A passion for gaining insight into real-world datasets and clearly communicating through data visualization techniques.
• Interest in material discovery, computer vision, handling big data and optimisation techniques.
• Highly effective communicator who encourages innovation through collaboration.
• Natural problem-solver with a desire to learn.
• Organised and self-motivated.

Responsibilities

• Design, develop and validate novel machine learning models to optimize manufacturing processes and material composition.
• Collaborate closely with process engineers, material scientists and other domain experts to identify and engineer the most meaningful features.
• Develop Alloyed's machine learning platforms to facilitate adoption and application of validated models.
• Work as part of a fast-paced, agile development team.
• Identify and prioritise opportunities to rapidly deliver new capabilities.
• Build and maintain robust MLOps pipelines to support 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.

Skills

Python development

Machine learning

MLOps

Airflow

MLflow

Containerization (Docker)

Data visualization

Big data

Bayesian modelling

Education

BSc in science/engineering/mathematics/computer science (2:1 minimum)

Tools

Airflow

MLflow

Docker

Job description

Company Overview Alloyed is a young venture-funded company of about 150 world-class metallurgists, engineers, and software developers working across 3 sites in the UK, 1 in Japan, and 1 in Seattle to build the future of advanced metal components. To do this it uses proprietary software packages which combine advanced machine learning and physical modelling to invent better alloys, devise better ways to process them, and design better 3D-printed components. Responsibilities Design, develop and validate novel machine learning models to optimize manufacturing processes and material composition Collaborate closely with process engineers, material scientists and other domain experts to identify and engineer the most meaningful features Develop Alloyed's machine learning platforms to facilitate adoption and application of validated models Work as part of a fast-paced, agile development team Identify and prioritize opportunities to rapidly deliver new capabilities Build and maintain robust
MLOps pipelines to support 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 Bachelor's degree in science, engineering, mathematics or computer science (2:1 minimum) Strong python development skills Practical experience in the development of machine learning models and/or deep learning to solve complex science and engineering problems Experience with MLOps tools and practices, including Airflow, MLflow, and containerization (e.g., Docker) A passion for gaining insight into real-world datasets and clearly communicating through data visualization techniques Interest in material discovery, computer vision, handling big data and optimisation techniques Highly effective communicator who encourages innovation through collaboration Natural problem-solver with
a desire to learn Organised and self-motivated Desired Skills Master's degree in machine learning, mathematics or statistics Understanding of probabilistic model development Experience of Bayesian modelling Good understanding of software design principles and best practices Good knowledge of at least one object-oriented language Familiarity with cloud platforms (e.g., Azure, AWS, GCP) and infrastructure-as-code tools (e.g., Terraform)