AI Research Engineer - 3D Computer Vision

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 specialising in scene understanding and localisation, responsible for building systems for scene matching, geo-registration, simultaneous localisation and mapping (SLAM), and 3D reconstruction. 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:

Developing and benchmarking scene matching and geo-registration pipelines robust to challenging conditions such as changes in illumination, viewpoint, season, or sensor modality.

Building and evaluating SLAM, visual odometry, or structure-from-motion systems for deployable platforms under real-world operational constraints.

Researching and adapting state-of-the-art geometric deep learning or feature matching methods to concrete use cases, from local feature descriptors to learned place recognition.

Collaborating with product and downstream deployment teams to integrate localisation and reconstruction outputs into navigation or decision-making pipelines and shape the roadmap for new capabilities.

The day-to-day You will develop computer vision models and pipelines that leverage and extend the latest state-of-the-art methods and architectures, as well as design experiments and conduct benchmarks to evaluate and improve their performance in real-world scenarios. You will also apply and develop techniques to adapt them to the target hardware and constraints associated to the downstream ML/AI tasks. You will contribute to impactful projects and will collaborate with people across several teams and backgrounds. 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 thoroughly evaluating advanced AI-based systems.

Have hands-on experience developing localisation, scene matching, or 3D reconstruction systems. You have iterated on models and geometric pipelines beyond benchmarks and understand what it takes to make these systems reliable under real-world data distributions and deployment constraints.

Are deeply familiar with modern approaches to geometric computer vision and deep learning, including learned feature matching, place recognition, visual SLAM, visual-inertial odometry (VIO), or neural 3D representations such as neural radiance fields (NeRF), and have applied techniques such as domain adaptation or model compression to practical problems.

Possess solid software engineering skills, writing clean and well-structured code in Python, and have experience deploying AI software to production including testing, QA, and monitoring.

Have excellent communication skills and the ability to report and present research findings clearly and efficiently, both internally and externally.

Are passionate about keeping up to date with current research and enjoy reimplementing and extending state-of-the-art approaches in geometric deep learning, SLAM, and 3D vision.

Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points. Nice to have

PhD in computer vision, machine learning, robotics, or a related field, with publications in top-tier venues (e.g. CVPR, NeurIPS, ICLR, ICCV, ICRA,