Applied AI Scientist – Computer Vision (3D Reconstruction & High-Frequency Imaging)
Team: Applied AI – Vision, Modelling & Experimentation
Type: Full-time | Multiple seniority levels
About the Role
We 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 Do
* Develop 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 Requirements
* Strong hands-on experience in applied computer vision, including several of the following:
* Image segmentation & object detection
* 3D reconstruction or geometry-based modelling
* High-frequency or high-volume video analytics
* Spatiotemporal modelling or generative vision
* Proficiency 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.