Research Engineer

NYU Grossman School of Medicine is one of the nation's top-ranked medical schools. For 175 years, NYU Grossman School of Medicine has trained thousands of physicians and scientists who have helped to shape the course of medical history and enrich the lives of countless people. An integral part of NYU Langone Health, the Grossman School of Medicine at its core is committed to improving the human condition through medical education, scientific research, and direct patient care.

At NYU Langone Health, equity and inclusion are fundamental values. We strive to be a place where our exceptionally talented faculty, staff, and students of all identities can thrive. We embrace inclusion and individual skills, ideas, and knowledge.

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Position Summary:

We have an exciting opportunity to join our team as a Research Engineer.

This position offers an exceptional opportunity to contribute to groundbreaking research focused on advancing the science of organ transplantation and shaping national organ allocation policy. Under the mentorship of internationally renowned leaders Dr. Dorry Segev, MD, PhD, Dr. Sommer Gentry, PhD, and Dr. Michal Mankowski, PhD the Research Engineer will join a multidisciplinary team committed to addressing critical challenges in organ transplantation and improving patient outcomes.

They will have access to state-of-the-art resources and collaborate on projects that combine epidemiology, operations research, qualitative methods, and research engineering methods.

This position will contribute towards the Operations Research Core of the Center for Surgical & Transplant Applied Research (CSTAR) that is a part of the Department of Surgery and Transplant Institute. This individual will be positioned to contribute to analytics efforts within CSTAR and with all internal and external collaborators. By teaming up with clinicians, operations and administrative teams, the individual will develop innovative and impactful data-driven healthcare solutions.

The Research Engineer will report to Dr. Michal Mankowski while working closely with other staff and faculty members of CSTAR.

Job Responsibilities:

* Design, implement, and maintain the backend and frontend of the software infrastructure and software pipelines necessary to facilitate frictionless machine learning research and development

* Design, implement, and maintain the necessary tools which can assist in translating the research into medical devices deployed in real clinical workflow

* Work closely with investigators, product managers, and other engineers to build the roadmap to accomplish the vision of the CSTAR

* Act as a project leader and be responsible for gathering requirements in collaboration with investigators, creating project plans, and leading its execution

* Lead technical projects, help recruit and mentor a team of junior software engineers

* Provide technical guidance to faculty and lab staff on projects at the intersection of engineering and biomedical research

Minimum Qualifications:

To qualify you must have a Masters degree in computer engineering or a bachelor's degree in computer engineering, or equivalent, with at least 2 years of relevant work experience

Familiarity with machine learning principles, statistics, probability, and numerical optimization

Some experience building machine learning models using large-scale datasets, and deploying them in real world environments

Experience developing and debugging in scripting languages such as Python

Familiarity programming in lower-level languages such as C/C++ Experience working with standard machine learning and computer vision libraries, such as PyTorch, Tensor Flow, and OpenCV.

Preferred Qualifications:

Experience in building software within a large healthcare system

Experience with building and deploying systems in real world environments for data collection, aggregation, and preparation

Experience in building tools for high performance computing and distributed systems

Experience in building both frontend and backend software tools that can facilitate ML research

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