Job Opportunities API

Machine Learning Engineer Location: Hybrid Company: Ferritico Employment type: Full-time Ferritico is looking for a Machine Learning Engineer to design, develop, deploy, and continuously improve machine learning solutions for advanced materials and steel applications, with a strong focus on production-ready models, data workflows, cloud services, and product integration. This is a hands-on technical role for someone who enjoys working at the intersection of machine learning, software engineering, data, and industrial product development. About the role You will contribute to the development of Ferritico's machine learning models and software platform. The role involves turning industrial and materials data into robust model logic, reliable validation workflows, scalable cloud services, and user-facing product features. You will work closely with materials engineers, and customers to ensure that machine learning solutions are accurate, maintainable, well-documented, and aligned with real industrial needs. Key responsibilities Manage and organize the aggregation, cleaning, and preparation of materials, process, and property data in collaboration with materials engineers. Design and develop machine learning models and appropriate model structures. Define model assumptions, evaluation metrics, validation datasets, limitations, and acceptance criteria. Validate, benchmark, and continuously improve existing and future machine learning models. Develop and maintain cloud-based machine learning services, training workflows, and inference endpoints. Monitor production models and troubleshoot performance, reliability, and data-quality issues. Integrate new machine learning modules into Ferritico's web application. Support customers in running simulations, understanding model outputs, and identifying suitable machine learning solutions for their processes. Contribute to testing, technical documentation, code reviews, and engineering decision-making. What we are looking for We are looking for someone with a strong background in machine learning, data science, computer science, mathematics, engineering, artificial intelligence, or a related quantitative field. The ideal candidate has: An MSc, PhD, or equivalent practical experience in a quantitative field such as Computer Science, Mathematics, Engineering, Artificial Intelligence, or a related discipline. Strong proficiency in Python and experience building clear, maintainable, and well-tested code. Practical experience with pandas, scikit-learn, and common workflows for data preparation, model development, evaluation, and deployment. Solid understanding of statistical modeling, machine learning methods, validation strategies, and performance metrics. A basic understanding of backend and frontend development and how machine learning components integrate into software products. Rigorous attention to detail, strong communication skills, and the ability to take ownership of high-quality deliverables in a collaborative team. Nice to have Experience with any of the following would be highly valuable: Google Cloud Platform, cloud hosting, containerized services, or MLOps workflows. Git-based version control, automated testing, continuous integration, and production monitoring. Physics-informed machine learning, scientific computing, or models that incorporate domain constraints. Materials engineering, metallurgy, steel-industry data, or other industrial engineering applications. Customer-facing technical work, SaaS products, web applications, or translating business and process needs into machine learning solutions. This role could be a strong fit if you Have recently completed an MSc or PhD involving machine learning, statistical modeling, artificial intelligence, or scientific computing. Have practical experience developing, validating, deploying, or maintaining machine learning models. Enjoy combining data science with software engineering and practical product development. Are an ambitio