Applied AI Scientist – Computer Vision (3D Reconstruction & High-Frequency Imaging)Team: Applied AI – Vision, Modelling & ExperimentationType: Full-time | Multiple seniority levelsAbout the RoleWe are expanding our Applied AI division and are seeking Computer Vision Scientists to build high-impact imaging and modelling solutions across healthcare, biology, robotics, agriculture, and climate applications. This team translates cutting-edge research into real-world, deployable tools—working directly with domain experts to define problems, design models, and deliver production-ready pipelines.You will work on large-scale visual datasets, high-frequency sensor and video streams, and 3D reconstruction challenges to support projects spanning phenotyping, experiment automation, and autonomous lab systems. This role is hands-on, highly collaborative, and ideal for scientists who enjoy building practical CV systems that directly support scientific discovery.What You’ll DoDevelop and deploy advanced computer vision models for segmentation, detection, classification, 3D modelling, and spatiotemporal prediction.Build pipelines that process large-scale image and video datasets from drones, lab cameras, robotic platforms, and other high-volume sensor systems.Implement vision modules for autonomous experimentation, experiment tracking, and sensor-driven workflows.Work with cross-functional teams—AI Research, Data Engineering, and Robotics—to integrate models into real systems.Apply adaptive experimentation and Bayesian optimisation methods to guide data acquisition, experiment selection, or sensor-driven decision-making.Ensure engineering-grade code quality, reproducibility, and scalable deployment across compute clusters.Collaborate directly with scientists and research partners to translate real-world needs into actionable vision models.Core RequirementsStrong hands-on experience in applied computer vision, including several of the following:Image segmentation & object detection3D reconstruction or geometry-based modellingHigh-frequency or high-volume video analyticsSpatiotemporal modelling or generative visionProficiency with modern CV frameworks: PyTorch, TensorFlow, OpenCV, vision transformers, 3D geometry libraries.Experience working with large-scale visual datasets and building production-grade vision pipelines.Ability to form independent scientific/technical conclusions and pressure-test results with domain experts.Strong coding standards beyond notebooks—clean, scalable, and deployable ML engineering practices.