Senior Staff AI Data/Validation Engineer

Work Flexibility: Remote

What You Will Do:

 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.

What You Will Need:

Required Qualifications:

• Bachelor's Degree in Computer Science, Machine Learning, Electrical Engineering, Mathematics, Statistics, Bioengineering, Biomedical Engineering, or related field
• 6+ years of experience in computer vision and deep learning / machine learning development, 4+ years of work experience required if Master's Degree in the above field(s) and 2+ years of experience with PhD in above field(s)
• 2+ years of experience in statistic and descriptive data analysis / data science to help drive data-driven decision making.

Preferred Qualifications:

• Experience with Python or similar scripting and statistical language
• Substantial experience in the development and validation of AI models, specifically within the medical device or healthcare industry. This experience should demonstrate a deep understanding of the entire model lifecycle, including: Data Acquisition and preprocessing and labeling, model training and tuning, Model evaluation and performance, bias detection and mitigation, QMS documentation
• Strong foundation on Statistics and descriptive data analysis to help develop data-driven scientific rigor to AI model validation process, including experience with confidence intervals development, power analysis, statistical tests, etc., particularly when dealing with AI/ML model performance evaluation
• Experience in handling, organizing, categorizing big dataset with any commercial tools
• Experience with AI/ML frameworks such as PyTorch, OpenCV, TensorFlow, scikit-learn etc. for model training and development and validation
• Experience with medical devices and product development in a regulated industry, e.g., software developed under ISO 13485

• $100k - $215k salary plus bonus eligible + benefits. Actual minimum and maximum may vary based on location. Individual pay is based on skills, experience, and other relevant factors.

 

Travel Percentage: 10%

Stryker Corporation is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, gender identity, sexual orientation, national origin, disability, or protected veteran status. Stryker is an EO employer – M/F/Veteran/Disability.

Stryker Corporation will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.

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