Machine Learning Engineer

Role overview

As a Machine Learning Engineer at Radformation, you will build and improve machine learning models that directly support clinical workflows and patient outcomes for AI-driven radiotherapy products. You will work closely with AI, cloud, research, and product teams to develop scalable data pipelines, improve model performance, and help support regulatory submissions for medical device software.

Responsibilities

• Design, build, and maintain robust ETL pipelines for AI model development and deployment
• Develop, train, and optimize machine learning models for radiotherapy software
• Partner with product and research teams to take new AI-driven features and algorithms into production
• Support FDA submissions with contributions to documentation, validation, and regulatory processes
• Participate in design reviews and risk analyses to ensure safe, effective products
• Mentor junior engineers and data scientists and foster a collaborative environment

Requirements

• MS in Computer Science, Mathematics, Statistics, or related field
• 3+ years of experience
• Expert-level Python proficiency
• Hands-on experience building, training, and tuning ML models
• Strong experience with PyTorch and/or TensorFlow
• Experience with CNNs, including U-Net architectures
• Experience using Git and modern code repositories (e.g., GitHub, Bitbucket, Azure DevOps)

Preferred qualifications

• Experience with medical imaging and image processing (segmentation, resampling, smoothing)
• Familiarity with clinical data standards such as DICOM or HL7
• Experience working in regulated environments (e.g., HIPAA, FDA, medical device software)
• Familiarity with modern AI-assisted development tools (e.g., Cursor, Claude Code, Codex)

Location/remote

• Fully remote team