Ferritico AB jobb

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 ambitious and independent learner who takes responsibility for results while collaborating closely with others. Are excited about helping shape digital tools for the future of steel and advanced materials. Why join Ferritico? At Ferritico, you will join a Swedish software startup working at the frontier of materials science, AI, and industrial digitalization. Built on more than 10 years of research at KTH, our SaaS platform helps steel companies accelerate the development, manufacturing, and implementation of advanced alloys. You will have significant responsibility and autonomy, work with a small multidisciplinary team, and influence both the machine learning foundation and product direction of a platform used in industrial production. We value teamwork, curiosity, technical excellence, and clear communication. Not sure you meet every requirement? We encourage you to apply even if your experience does not match every qualification listed above. We value diverse backgrounds, different perspectives, and people who are motivated to learn and contribute. How to apply Please send your CV and a short note describing your motivation for the role, along with your relevant experience in machine learning, data science, software engineering, or industrial applications, to: [email protected] (Please include “Machine Learning Engineer” in the email subject line) Application deadline: 31 July 2026

Senior Materials Informatics Engineer Location: Hybrid Company: Ferritico Employment type: Full-time Ferritico is looking for a Senior Materials Informatics Engineer to develop, validate, and productize physics informed machine learning models for advanced materials, with a strong focus on steel, metallurgy, heat treatment, phase transformations, and process-property relationships. This is a mid- to senior-level technical role for someone who enjoys working at the intersection of materials science, computational modeling, machine learning, and product development. About the role You will contribute to the development of computational and machine learning models for metals and metallurgical applications. The role involves translating materials science expertise into clear model logic, validation workflows, technical requirements, and product features. You will help ensure that scientific models are technically sound, well-documented, validated, and aligned with real industrial engineering needs. Key responsibilities Develop computational and ML-based models for steel metallurgy, heat treatment, transformation temperatures, phase transformations, microstructure evolution, and process-property relationships. Define model assumptions, expected behavior, validation datasets, limitations, and acceptance criteria. Improve AI model performance by integrating materials science expertise and physics-informed modeling approaches. Identify data correlations between process, structure, and properties in steel processing and industrial applications. Retrieve, structure, clean, and curate materials data from literature, synthetic datasets, experiments, and other sources. Use Python-based workflows for data treatment, model execution, input preparation, output analysis, and visualization. Test AI software outputs and benchmark results against other simulation or modeling tools. Communicate model behavior, limitations, and results clearly to both technical and non-technical stakeholders. Contribute to documentation, testing, validation reports, and technical decision-making. What we are looking for We are looking for someone with a strong background in materials science, computational materials science, or physical metallurgy. The ideal candidate has: A PhD in Materials Science, Metallurgy, Computational Materials Science, or a related field; or an MSc with relevant experience in materials modeling. Strong understanding of physical metallurgy, preferably including steels, heat treatment, phase transformations, microstructure evolution, and process-property relationships. Experience developing, using, or validating computational models for materials behavior. Familiarity with materials data, alloy compositions, thermal histories, transformation kinetics, or microstructure-property relationships. Ability to use Python for scientific scripting, data treatment, and model prototyping. Strong communication skills and the ability to explain complex materials concepts in a multidisciplinary team. Nice to have Experience with any of the following would be highly valuable: Steel phase-transformation modeling, TTT/CCT diagrams, JMAK kinetics, carbide precipitation. CALPHAD, pycalphad, or similar tools. Alloy design, ICME workflows, ML applied to materials science. Product ownership, technical leadership, or experience translating scientific models into user-facing tools. Data analysis using NumPy, pandas, matplotlib, scikit-learn, Excel-based workflows, or similar tools. This role could be a strong fit if you Recently completed a PhD involving computational or experimental materials modeling. Have an MSc and several years of experience building or using materials models. Enjoy combining materials science with scripting, data analysis, and practical software development. Want to work close to product development rather than only research. Are excited about helping shape digital tools for the future of steel and advanced materials. Why join Ferritico? At Ferritico, you will be part of a Swedish software startup working at the frontier of materials science, AI, and industrial digitalization. You will have the opportunity to influence both the scientific foundation and the product direction of tools used for advanced materials development and manufacturing. We value teamwork, curiosity, technical excellence, and clear communication. Not sure you meet every requirement? We encourage you to apply even if your experience does not match every qualification listed above. We value diverse backgrounds, different perspectives, and people who are motivated to learn and contribute. How to apply Please send your CV and a short note describing your motivation for the role, along with your relevant experience in materials science, metallurgy, computational modeling, or materials informatics, to: [email protected] (Please include the job title in the email subject line)