AI Research Engineer - Computer Vision

Job description

Who we are Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards. As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously. We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications. The role At Helsing we deliver AI-based capabilities and the enabling foundation that allow machines to perceive and assist human decision-making. You will have the unique opportunity to shape AI capabilities in one of the most challenging sectors, where high generalisation capabilities need to be paired with hardware constraints and robustness against adversarial attacks. You will be part of a computer vision team responsible for building models for object detection, recognition, classification, and tracking. You will develop and extend state-of-the-art architectures and pipelines, design rigorous experiments, and conduct benchmarks to evaluate and improve real-world performance, including adaptation to the compute constraints and operational requirements of downstream deployments. You will collaborate across research, engineering, and product teams on high-impact projects. Typical examples of day-to-day responsibilities include, depending on the team: Optimising models for real-time deployed performance on target hardware: selecting the best architecture or layers for throughput and latency constraints, curating and leveraging data to improve performance Researching new model architectures, replicating and adapting SOTA detection, tracking, or classification methods to concrete use cases according to operational needs Running multi-GPU or multi-node training jobs on large-scale sensor datasets to build powerful foundation or auto-annotation models Interacting with product and downstream deployment teams to identify perception bottlenecks, prioritise improvements, and shape the roadmap for new capabilities You should apply if you Hold an MSc in computer science, machine learning, robotics, or a closely related field, with experience in designing, implementing, and