ABOUT THIS POSITION

AI is transforming industries, but in critical environments, it must be reliable, scalable, and secure. At TNO, we develop AI-driven systems that interact with physical processes (sensors, actuators), and make autonomous decisions in complex, real-world scenarios—from adaptive energy grids and defence intelligence networks to biodiversity digital twins that support environmental policy. As a Machine Learning Engineer, you will contribute to the research, development, and implementation of machine learning solutions that directly impact vital infrastructure. You will work with leading experts in AI, distributed systems, and software engineering to develop innovative applications that bridge the gap between fundamental research and practical deployment. Aspects such as assessing and improving system performance, safety, and controllability will be part of your work. The research group Intelligent Cyber-physical Systems Engineering (ICSE) is your home base. From Groningen and The Hague, more than 35 scientists, researchers, and consultants prove the added value of digital applications every day. You’ll also work closely with colleagues from other departments and units. ICSE is a great mix of highly qualified women and men with different levels of experience and of various nationalities. We all have a passion for applied research and we enjoy talking about it in an open and informal atmosphere we’re there for each other, support each other, and are open to your ideas!

As a Machine Learning Engineer, you will be involved in the full lifecycle of Machine Learning solutions, from research and experimentation to building and deploying robust Machine Learning models and AI-based systems. Your role includes:

• Developing Machine Learning software that enhances autonomy in cyber-physical systems.
• Building proof-of-concept applications and demonstrators that translate AI research into practical, scalable solutions.
• Optimizing and deploying Machine learning models, ensuring performance, reliability, and efficiency.
• Collaborating with multidisciplinary teams, including experts in AI, systems engineering, and domain-specific applications.
• Exploring cutting-edge AI techniques and evaluating their real-world applicability in critical infrastructure and in when interacting with physical processes.
• Work with customers and partners to solve new and existing problems using state-of-the-art Machine Learning technology.