About the job

Neurolabs is hiring a Staff Computer Vision Engineer to help build the future of real-time retail execution by scaling its advanced Image Recognition technology, powered by synthetic data and digital twins, into a robust, production-grade system that gives global CPG brands immediate visibility into in-store performance without manual audits, retraining or delays. In this highly technical and hands-on role, you will lead the design, training, optimisation and deployment of computer vision models across detection, recognition, segmentation and tracking tasks while balancing performance, latency and reliability for diverse, real-world shelf conditions. You will play a key part in evolving Neurolabs’ large vision model (LVM), improving scalability and adaptability across multimodal use cases, advancing synthetic data generation pipelines, and rigorously testing and productionising new approaches using frameworks like PyTorch or TensorFlow. Working cross-functionally with product, infrastructure and customer teams, you will ensure models integrate seamlessly into workflows, align technical direction with business goals, and contribute technical leadership without direct line management. The ideal candidate holds a PhD or equivalent experience in Computer Vision, Machine Learning or a related field, with a proven record of deploying large-scale CV models, strong Python programming skills, deep understanding of modern architectures such as transformers, and familiarity with multimodal data and MLOps tools. Neurolabs offers a competitive open salary structure up to £150,000 per year, equity share options, hybrid working with access to a vibrant London office, 25 days annual leave plus bank holidays, comprehensive private medical insurance including dental, optical and travel cover, inclusive culture, company retreats, and visa sponsorship under the UK Skilled Worker route, making this a unique opportunity to contribute to cutting-edge vision AI while growing your career in a supportive, outcome-driven environment.