Alloyed is a young venture funded company of about 180 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.

• 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 Alloyeds 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 CICD practices and model governance

• Bachelors 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 such as 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

• Masters 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 such as Azure, AWS, GCP and infrastructure as code tools such as Terraform