Job

• Bachelor's or master's degree in engineering, computer science, biomedical/life sciences, healthcare, management, or a related field


• Experience coordinating multidisciplinary, technical, research, or healthcare projects


• Working literacy in machine-learning workflows — data collection, annotation, training, evaluation, validation. You won't build models, but you must be able to follow (and question) a discussion about them.


• Excellent written and verbal communication; strong attention to detail and follow-through


• The ability to turn broad, ambiguous objectives into clear, actionable plans

Seeking applications from candidates who can lead multidisciplinary AI healthcare projects, coordinate clinicians and engineers, manage timelines, publications, and deliver AI solutions that improve patient care. Job responsibilities include:

• Running the engineering cadence. Daily stand-ups with the ML team, sprint planning, retrospectives — keeping five engineers (plus student researchers) unblocked, prioritized, and honest about progress.


• Running the clinical interface. Doctors join us every two weeks. You prepare those meetings, capture what the clinicians actually need (which is not always what they first say), and translate it into requirements engineers can build against — then translate results back into language clinicians trust.


• Owning the plans. Turning ambiguous research goals ("can we predict severity from these videos?") into milestones, owners, and timelines — and updating them when reality disagrees, which it will.


• Protecting the deadlines. Conference submissions run this lab's calendar. You track them, work back from them, and raise the alarm early when a project is quietly drifting off course.


• Sharpening the output. Reviewing manuscripts, reports, and proposals for clarity and consistency. You are often the last set of eyes before something leaves the building.


• Asking the uncomfortable question. When a result looks too good, when two people are measuring different things, when "almost done" has been almost done for three weeks — noticing is your job. You don't need to solve the technical problem; you need to make sure it can't hide.