ai engineer in maritime AI

Ocean Infinity uses robotics, autonomous technology, and software to transform complex maritime operations. Its uncrewed systems support safer, smarter, and more sustainable ocean exploration and operations while reducing environmental impact.

• Contribute to the design and delivery of AI solutions from exploratory research through production-ready inference pipelines;
• Define technical approaches for complex AI projects, including model architectures, data strategies, evaluation frameworks, and deployment paths;
• Design and implement models for detection, classification, prediction, and feature extraction;
• Work with large-scale, often unlabelled datasets and apply self-supervised and semi-supervised learning techniques;
• Adapt and fine-tune foundation models for domain-specific maritime AI challenges;
• Review code, define best practices, and champion high engineering standards across the AI team;
• Communicate technical progress, trade-offs, and results to technical and non-technical stakeholders.

• Degree in Computer Science, Mathematics, or a related field;
• Hands-on experience in AI and Machine Learning;
• Strong Python development experience with PyTorch, TensorFlow, or similar deep learning frameworks;
• Experience working with non-standard imagery data, including writing custom data loaders and curating custom datasets;
• Ability to write clean, maintainable code and maintain software engineering quality alongside model performance;
• Ability to work with high autonomy in ambiguous, evolving environments and break large problems into deliverable increments;
• Ability to communicate effectively with technical and non-technical stakeholders and explain complex AI concepts clearly;
• Nice to have: PhD or research background in Machine Learning or underwater sensor processing algorithms, self-supervised learning such as DINO-style training or JEPA, foundation model adaptation through fine-tuning or few-shot methods, sonar or magnetometer or acoustic or other remote-sensing data, MLOps practices including experiment tracking, model versioning, containerisation, and CI/CD pipelines, publications or contributions through journals, conferences, Kaggle competitions, or open-source projects.

• Salary £85,000 - £90,000 Per Annum.