AI Robustness Engineer

2 days ago


London, Greater London, United Kingdom Safe Intelligence Full time
About the Company:
Safe Intelligence is a pioneering firm dedicated to making AI safe and reliable for everyone. Our mission is to develop advanced deep validation techniques and tools that enable AI/ML engineers worldwide to validate the robustness of their models and repair fragilities. In this exciting role, you will play a leading part in helping both customers and driving our product forward by applying advanced validation techniques to machine-learning models. Your focus will be on tabular data models used in finance and insurance, but there will be opportunities to branch out to other model types and industries. You will work closely with customers and end-users to understand their ML models and help them assess performance. This includes R&D and product teams at customer organisations. Additionally, you will implement prototypes, use cases, and solutions that apply the algorithms developed at Safe Intelligence to address user-specific problems, particularly in the field of tabular data for Finance and Insurance. You will also conduct experiments to evaluate various approaches and weigh their respective trade-offs. Furthermore, you will coordinate with the research and platform teams to guide future development based on use-case-specific challenges. Finally, you will contribute to the development of an efficient and scalable package for performing verification and robust learning. As a Data Validation Expert, you will have the opportunity to advance the techniques, bring advanced technologies to AI/ML engineers worldwide, and contribute to our shared mission to realise successful and reliable AI. The technical requirements for the role include experience in training, evaluating, and deploying Machine Learning models in the Finance or Insurance industries. Familiarity with Python and the packages widely used in data science and machine learning, including NumPy, pandas, scikit-learn, TensorFlow, and PyTorch, is also essential. Furthermore, familiarity with common machine-learned model types used for tabular data and fluency in validation and evaluation frameworks and metrics frameworks for machine learning, such as Accuracy, Recall, F1 scores, and others, are required. An in-depth understanding of Neural Networks and Decision Trees enabling you to train such models to high performance and modify/tune their architecture based on given constraints is also necessary.

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