We are looking for an experienced and highly skilled Senior Staff AI Data and Validation Engineer. A successful candidate will be responsible for developing methods and processes for scientific evaluation of AI/ML models used in medical devices, ensuring compliance with all applicable FDA guidelines and regulations. This role is crucial for maintaining the safety, effectiveness, and quality of AI-enabled medical devices throughout their product lifecycle. The engineer will contribute to the design, execution, and analysis of validation studies, ensuring that the devices perform predictably and reliably for their intended use and across all relevant demographic groups, identify potential bias, and placing a strong emphasis on comprehensive data management practices, including data collection, processing, annotation, storage, and use. This includes ensuring data integrity, quality, and representativeness to mitigate potential biases and demonstrate the safety, effectiveness, and quality of AI-enabled medical devices throughout their lifecycle. Create detailed validation plans, protocols, and reports for AI-enabled medical device software functions encompassing both performance validation and model evaluation. This includes defining the scope of testing, performance metrics, and acceptance criteria Oversee the management of data used in both the development and validation of AI models, ensuring the quality, diversity, and independence of datasets. This involves assessing data representativeness, addressing potential biases, and ensuring the appropriate separation of training and test datasets Work closely with the product development team to ensure a comprehensive approach to transparency and bias is taken throughout the total product lifecycle Critically evaluate the technical characteristics of AI models, including their architecture, features, and parameters, as well as the algorithms used in their development. A comprehensive evaluation will include assessing the quality control and data management methods used to produce the AI-enabled device Develop and implement comprehensive risk assessment strategies for AI models, considering potential and unintended biases, limitations, and cybersecurity vulnerabilities Conduct rigorous performance validation studies, analyzing performance across different subgroups of the intended user population. Prepare and review documentation for regulatory submissions related to AI model validation, including 510(k) submissions, De Novo requests, and PMA applications. Implement strategies to address transparency and bias throughout the device lifecycle, from design to post-market surveillance Prepare comprehensive documentation for regulatory submissions of AI/ML-enabled medical devices, including detailed descriptions of the device, model development, validation studies, data management, and risk assessments Stay abreast of the latest FDA guidelines, regulations, and industry best practices related to AI model validation in medical devices.