Machine Learning Engineer II/III (Applied Research & Model Development)

PathAI is dedicated to improving patient outcomes with AI-powered pathology. They are seeking Machine Learning Engineers (Applied Research & Model Development) to develop and deploy machine learning models that advance medicine and improve patient care, collaborating with various teams across biomedical data science and product development.

Responsibilities:

• Design, develop, and deploy machine learning models for research and product development projects
• Collaborate cross-functionally with scientists, engineers, and product teams to translate biological and clinical requirements into scalable ML solutions
• Contribute to experimental design and analysis, including ideation, documentation, and reporting
• Participate in knowledge sharing and team initiatives (e.g., design reviews, journal clubs, ML best practices, governance activities)
• Improve ML pipelines and infrastructure in partnership with MLOps and platform teams
• Publish and present scientific work, supporting abstracts, manuscripts, and conference contributions

Requirements:

• Master's degree plus 2–4 years of experience, or Ph.D. with 0–2 years of experience for MLE II
• Proven track record of developing and deploying machine learning models into production or research applications
• Strong proficiency in Python, ML frameworks, and data pipeline development
• Demonstrated ability to work independently on projects, contribute to experimental design, and improve ML workflows
• Strong communication skills and ability to collaborate across scientific and engineering teams
• Master's degree plus 5+ years of experience, or Ph.D. with 3+ years of experience for MLE III
• Deep expertise in ML, computer vision, or biomedical AI, with a history of high-impact contributions (publications, open-source, or products)
• Mastery of ML frameworks, software engineering best practices, and deployment pipelines
• Ability to lead end-to-end projects, mentor others, and set technical direction
• Experience articulating technical improvements into business or clinical impact
• Strong record of contributions to scientific strategy (abstracts, manuscripts, conference presentations)