Alloyed is a young venture-funded company of around 100 world-class metallurgists, mechanical engineers, technicians, and software developers working across three offices in the UK and one in the US, building the future of advanced metal components. We use proprietary software packages which combine advanced machine learning and physical modelling, as well as extensive experimental facilities, to 3D print metal components better and faster than anyone else.

At our premises in Yarnton and Abingdon, near Oxford, we are aiming to build the world’s fastest, smartest, and best-equipped facility for the rapid development of additively manufactured parts for the electronics, aerospace and industrial sectors, and novel metal alloys for better performance.

• 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

• 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

• 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)