Machine Learning Engineer

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)

How to Apply

To apply, submit a CV and a supporting statement by email to . The supporting statement should explain how you meet the selection criteria for the role using examples of your skills and any experience. All documents should be uploaded as PDF files. Please provide details of two referees and indicate if we can contact them now.

This is a continuous recruitment effort. We will fill positions as excellent candidates present themselves so your early application is encouraged.