Research Engineer Job New York New York USA,Software Development

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.

We are looking for a highly motivated and passionate Machine Learning Research Engineer to join our team who has expertise in machine learning, computer vision, and software engineering. This is a unique opportunity to work in the interdisciplinary area of machine learning and healthcare as you will not only work on developing machine learning models but will also have an opportunity to deploy the models in real clinical workflows, thereby enabling you to make a real-world impact by saving and/or improving the lives of real people.

As an ML Research Engineer, you will be a part of an interdisciplinary group of research scientists, clinicians, and engineers. In close collaboration with your colleagues, you will help develop, validate, and deploy machine learning models. You will be responsible for building and maintaining the necessary infrastructure and software pipelines to facilitate this work. You will engage with interdisciplinary research topics and develop machine learning solutions for various complex and novel problems using large scale medical imaging datasets and datasets of other modalities.

Lastly, you will operate under standard procedures and protocols in regulatory and compliance.

Job Responsibilities:

Job Responsibilities will include (but are not limited to):

Contribute to the design, training, and evaluation of large-scale foundation models integrating multiple modalities (e.g., text, images, genomics, medical records);

Be responsible for parts of pipeline associated with developing such models, including extraction and curation of the datasets, implementing and training/testing/validating the machine learning models to ensure clinical relevancy;

Be involved in model analyses to understand their deficiencies and thereby to propose refinements to improve end outcomes;

Be involved in data analysis to extract patterns in large swaths of data to acquire insights into the nature of the data, in turn enabling us to model it better;

Create and maintain the necessary infrastructure and software pipelines;

Adapt machine learning and neural network algorithms and architectures to best exploit modern parallel environments, such as GPUs and distributed clusters;

Ensure Standard Operating Procedures and protocols are developed, maintained and applied that comply with regulatory mandates of all appropriate/applicable governing bodies.

Minimum Qualifications:

To qualify you must have a
Masters degree in computer science, computer engineering, biomedical engineering, applied mathematics, or a related technical field; or a bachelors degree with at least one year of relevant work experience
Strong foundation in machine learning, statistics, probability, numerical optimization, and experimental design
Experience building machine learning models, preferably using large-scale datasets and deploying them in real-world environments (on-premise or in the cloud)
Expertise in modern machine learning frameworks, and CV and NLP libraries (PyTorch, Tensorflow, scikit-learn, transformers, OpenCV, etc.)
Proficiency developing and debugging in scripting languages such as Python
Ability to work independently while collaborating effectively in a multidisciplinary team environment

Preferred Qualifications:

Familiarity with radiology standards and software is beneficial but not required (DICOM, NIfTI, PACS, FSL, etc)
Prior exposure to, or some domain knowledge of, machine learning applications in medical imaging and/or medical text

Qualified candidates must be able to effectively communicate with all levels of the organization.

NYU…