Machine Learning Engineer

Radformation is seeking a Machine Learning Engineer to build and improve ML models for radiotherapy software that impacts clinical workflows and patient outcomes. The role focuses on ETL/data pipelines, ML model development and deployment, and supporting FDA submissions in a regulated environment.

• Design and maintain ETL pipelines for AI model development and deployment
• Develop, train, and optimize machine learning models for radiotherapy software
• Collaborate to bring new AI-driven features and algorithms into production
• Support FDA submissions via documentation, validation, and regulatory processes
• Participate in design reviews and risk analyses and mentor junior engineers

You will design robust ETL pipelines, develop and tune ML models using Python with PyTorch and/or TensorFlow, and implement convolutional neural networks including U-Net architectures. You will integrate models into production workflows and contribute to documentation, validation, and regulatory processes for FDA submissions.

The ideal candidate has an MS in a technical field and 3+ years of hands-on machine learning engineering experience, including building and tuning ML models using Python. They have strong PyTorch and/or TensorFlow experience (including convolutional neural networks and U-Net architectures) and can build ETL/data pipelines, while supporting FDA submission documentation in a regulated medical device environment.