Machine Learning Engineer

OrbDB Labs AB

What we're building OrbDB is building data infrastructure for AI reliability. For every prediction a model makes, the platform determines whether the model is sufficiently certain for the result to be acted on automatically or whether the case should be routed to a human reviewer. Today’s AI production systems are unable to distinguish which of their predictions are trustworthy. We are building the layer that allows organizations to automate the cases where automation is statistically justified, and to escalate the rest with confidence. OrbDB is founded and led by researchers with deep expertise in the underlying methods. The role You will work on the models that sit at the center of our platform. Our work is built around Graph Neural Networks, and the questions you will engage with are the ones that sit beneath the surface of any serious deep learning system: questions about architecture, training behaviour, optimization, and the relationship between what a model is doing and what we expect it to do. This is a role for someone who knows the fundamentals of deep learning well enough to reason about them from first principles, not from tutorials. You will work closely with our research-led founding team, and the questions you take on will move between the practical and the foundational, often within the same week. Unlike other AI startups, OrbDB builds on a mathematical foundation. So do the teams behind it. OrbDB Labs is a place where solid ideas and good taste matter more than loud voices. Specifically, you will: Train, evaluate, and improve the models that power the platform. Diagnose model behavior at a level deeper than metrics, and propose changes grounded in the underlying mathematics. Make principled choices about model design as required. Work alongside the engineering team to deliver research-grade models into a production system that customers can rely on. What we are looking for 2-4 years of experience working with deep learning models in a serious technical setting, whether in research, industry, or a combination. If you are close to that range and the rest of the role fits, we would still like to hear from you. A real command of the fundamentals of deep learning. You should be comfortable reading a paper, implementing it, and reasoning about why a model is or is not behaving as expected. Strong engineering skills. You write code that others can build on, and you understand that a model is only useful once it runs reliably. Fluency with the modern deep learning toolchain, particularly PyTorch. Genuine interest in the statistical foundations of what we are building. Concepts like Conformal Prediction and calibration should be ones you are eager to understand deeply. Useful, but not required Experience with Graph Neural Networks specifically, or with the libraries that support them (PyTorch Geometric, DGL, or equivalent). A graduate degree in a quantitatively rigorous field, or equivalent depth acquired through other means. Open-source contributions in the ML or deep learning ecosystem, particularly to production-grade libraries. Experience moving models from research code into production systems. This is a Stockholm-based hybrid role. Candidates must be living in or willing to relocate to the Stockholm area before starting.

Ferritico AB

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: contact@ferritico.com (Please include “Machine Learning Engineer” in the email subject line) Application deadline: 31 July 2026

Adyen

This is Adyen Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition.  For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. Machine Learning Engineer Adyen is hiring a Machine Learning Engineer for the Payments Core Data team in Bengaluru, our newest office. You will be responsible for designing, productionizing, and maintaining robust, scalable machine learning services that power data products at Adyen. In this role, you will:  * Build the Platform Bridge: Develop and maintain full-lifecycle production ML pipelines while acting as a technical anchor to adapt global frameworks for (India) localized requirements, ensuring architectural parity and high-performance execution. * Drive Applied Decision Science: Design and productionize end-to-end recommendation and classification models integrated directly into merchant-facing products, turning complex data patterns into real-time, actionable insights. * Optimize for Performance: Identify and resolve performance bottlenecks in training and inference (memory, latency, throughput) to ensure ML solutions scale seamlessly within a high-throughput production environment. * Architect the Intelligence Layer: Own the development of reusable AI components - that serve as the foundation for scaling Generative AI applications. * Champion Technical Excellence: Promote and apply software and data engineering best practices while partnering with international MLOps and platform departments to seamlessly adopt internal toolsets. Who You Are: * Experienced Engineer & Python Expert: You have 4+ years of experience in the machine learning domain with expert-level Python skills and deep familiarity with the standard data science toolkit (e.g., PyTorch, TensorFlow, XGBoost/LightGBM, Pandas, and Scikit-learn). * Full-Lifecycle Architect: You are proficient in the end-to-end ML lifecycle - from leveraging big data (Spark, SQL/Trino) to build robust pipelines to deploying and maintaining models in production using MLOps best practices. * Infrastructure & Automation Savvy: You have hands-on experience with ML infrastructure and orchestration tools such as Kubernetes, Docker, Airflow, Argo-Workflows, and monitoring stacks like Prometheus and Grafana. * Experimental & Iterative Mindset: You thrive in a "launch fast and iterate" environment, applying strong foundational knowledge of statistics and ML techniques to solve complex real-world problems. * Technical Leader & Communicator: You proactively take ownership of projects from ideation to deployment, with the ability to lead stakeholders and translate complex technical outcomes into clear insights for any audience. Nice to Have: * You have experience with distributed GPU compute environments * You have experience working with a Machine Learning ‘Feature Store’ Data positions at Adyen: We know companies handle different definitions for their data-related positions, this is for instance dependent on the size of a company. We categorized and defined all our positions. Have a look at this blogpost to find out!  Our Diversity, Equity and Inclusion commitments  Our unique approach is a product of our diverse perspectives. This diversity of backgrounds and cultures is essential in helping us maintain our momentum. Our business and technical challenges are unique, and we need as many different voices as possible to join us in solving them - voices like yours. No matter who you are or where you’re from, we welcome you to be your true self at Adyen.  Studies show that women and members of underrepresented communities apply for jobs only if they meet 100% of the qualifications. Does this sound like you? If so, Adyen encourages you to reconsider and apply. We look forward to your application! What’s next? Ensuring a smooth and enjoyable candidate experience is critical for us. We aim to get back to you regarding your application within 5 business days. Our interview process tends to take about 4 weeks to complete, but may fluctuate depending on the role. Learn more about our hiring process here. Don’t be afraid to let us know if you need more flexibility. This role is based out of our Bengaluru office. We are an office-first company and value in-person collaboration; we do not offer remote-only roles.