AI Machine Learning Engineer

JoltSynsor is a deep-tech spinout from Cambridge University, building the AI digital inspector to revolutionize infrastructure health monitoring. Our technology is solving a massive, global problem: the inefficiency and inaccuracy of manual inspections for critical infrastructure like bridges, tunnels, buildings and other large-scale assets.

Is this the next step in your career Find out if you are the right candidate by reading through the complete overview below.Every year, trillions of dollars are spent on infrastructure maintenance globally, yet catastrophic failures still occur due to human limitations in inspection processes. Our mission is to eliminate these inefficiencies and risks by replacing manual inspections with an AI-powered solution that’s faster, safer, and more reliable.Using our proprietary AI models and exclusive training data—developed through years of cutting-edge research—we enable real-time structural health monitoring at a fraction of the cost. This is a great opportunity to join a mission-driven company tackling one of the most significant challenges in sustainability, safety, and efficiency.The OpportunityWe are looking for an ML Engineer to join our Ox-bridge team, working with us to build this revolutionary platform from the ground up. It’s a great chance to be part of a game-changing journey—to create technology that impacts everyone's daily life worldwide while shaping the future of a fast-growing deep-tech startup.Qualifications:Bachelors in Computer Science, Data Science, Information Engineering, or a related field.Minimum of 2 years industry experience in Computer Science, Data Science, Information Engineering, or a related field.Technical skills:Proficiency in designing, training, and optimising neural networks, particularly CNNs.Strong background in handling and preprocessing large-scale point cloud datasets and high-resolution images.Knowledge of 3D feature extraction, denoising, and registration techniques for point cloud data.Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch). Familiarity with libraries Open3D and NumPy.Experience in designing and developing point cloud segmentation algorithms.Hands-on experience with benchmarking and evaluating deep learning models for anomaly detection, damage assessment, and/or segmentation.Desirable but not essential:Experience with data augmentation, synthetic data generation, and multimodal data integration.Experience with state-of-the-art architectures for point cloud processing and image analysis.Experience in cloud-based and distributed computing for model training (e.g., AWS, Google Cloud, Azure, or HPC systems).Other Skills:Interest and enthusiasm in working at the intersection of civil engineering and computer science.Experience collaborating in teams with engineers and/or data scientists.Problem-solving mindset and adaptability to new challenges.Attention to detail, particularly in data quality assessment and interpretation.Seniority level Entry levelEmployment type Full-timeJob function Engineering and Information TechnologyIndustries Technology, Information and Internet

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