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

PathAI's mission is to improve patient outcomes with AI-powered pathology. They are seeking Machine Learning Engineers to tackle unique challenges in advancing medicine and improving patient care, working closely with various teams to develop and deploy machine learning models.

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)