Machine Learning Engineer II​/III; Applied Research & Model Development

Our team is passionate about solving big challenges in healthcare and transforming the field of pathology with artificial intelligence.

PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials. Rigorous science and careful analysis is critical to the success of everything we do.

Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes.

We are seeking Machine Learning Engineers (MLE II, and III) to join our team and tackle unique machine learning challenges to advance medicine and improve patient care. You will work closely with teams across biomedical data science, product development, translational research, MLOps, and platform engineering to develop and deploy machine learning models for our AI products and services.

As an MLE at PathAI, your responsibilities will grow in scope as you progress through levels:

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.

Level-specific expectations:

MLE I:
Contribute to projects with guidance, implement models, and learn best practices.

MLE II
:
Independently deliver on projects, improve processes, and mentor junior engineers.

MLE III
:
Lead initiatives end-to-end, set technical direction, and identify new opportunities with clear business and scientific impact.

You will have the opportunity to work in a company where all employees put patients first. We believe that every team member provides valuable contributions to our success, and no task is too small for anyone if it's important to our company goals. Every PathAI employee is a contributor to our mission to pioneer better patient care by providing the best, most innovative AI tools to biotech, pathologists, clinicians and healthcare organizations.

You will work alongside and with leading innovators in the field of AI and medicine and you will play a critical role in product development to impact patient outcomes.

We welcome applicants across all MLE levels. Minimum qualifications differ by level:

MLE II:

Master’s degree plus 2–4 years of experience, or Ph.D. with 0–2 years of experience.

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.

MLE III

Master’s degree plus 5+ years of experience, or Ph.D. with 3+ years of experience.

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).