Applies advanced machine learning techniques to optimise manufacturing processes and material design. Builds scalable ML pipelines, collaborates with engineers and scientists, and enhances Alloyed’s machine learning platforms for additive manufacturing applications.

• Design, develop and validate novel machine learning models to optimise manufacturing processes and material composition.
• Collaborate closely with process engineers, material scientists and other domain experts to unlock the full potential of in-house data.
• Develop Alloyed’s machine learning platforms and packages to aid the additive manufacturing process.
• Help build automated pipelines using MLOps best practices to support scalable and reproducible model development, deployment, and monitoring.

• Bachelor’s degree in machine learning, science, engineering, mathematics, or related STEM field (2:1 minimum).
• Strong Python development skills, and an appreciation for software best practices.
• Practical experience in the development of machine learning models to solve complex problems.
• A passion for gaining insight into real-world datasets and clearly communicating through data visualisation 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.
• Experience or knowledge of FEA modelling, specifically for transient heat simulations.
• Experience with using deep learning techniques, particularly with time-series datasets.
• Familiarity with cloud platforms such as Azure, AWS, or GCP and infrastructure-as-code tools such as Terraform.
• Understanding of probabilistic model development.
• Experience with MLOps tools and practices, including Airflow, MLflow, and containerisation such as Docker.