Computer Vision Engineer

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.