Machine Learning Engineer

Our Center invites applications for a Machine Learning Engineer position focused on improving human health through novel applications of machine learning to medical imaging.

The Machine Learning Engineer will join our interdisciplinary team of medical imaging researchers at NYU Langone Health. We are looking for a highly motivated and passionate person with expertise in machine learning, computer vision, and software engineering.

Job Responsibilities

  • Collaborate with researchers and clinicians to develop highly scalable algorithms for medical imaging based on state-of-the-art machine learning techniques.
  • Own the entire pipeline associated with developing such models, including extraction and curation of datasets, implementation and training of machine learning models, testing/ validation of models to ensure clinical relevancy, and deployment of models in real clinical setting.
  • Contribute to model analysis to understand models’ deficiencies and propose refinements to improve outcomes.
  • Contribute to fundamental data analysis to extract patterns from large datasets in order to acquire insights that to further improve machine learning models.
  • Create project plans, meet deadlines, and resolve technical problems
  • Provide technical guidance to faculty and lab staff on projects at the intersection of machine learning and radiology.
  • 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.

Required Qualifications

  • master’s degree in computer science or equivalent
  • thorough understanding of fundamental principles in machine learning, probability theory, statistics, and numerical optimization
  • experience in building machine learning models for real-world problems involving large-scale datasets
  • experience in deploying machine learning models in real-world environments
  • experience in developing and debugging in scripting languages such as Python
  • familiarity with programming in low-level languages such as C/C++
  • experience working with machine learning and computer vision libraries, such as PyTorch, TensorFlow, and OpenCV
  • strong communication and leadership skills

Preferred Qualifications

  • technical understanding of the DICOM standard (NIfTI, FSL etc) and familiarity with radiology software, such as Vital, OsiriX, and PACS
  • prior exposure to or domain knowledge in machine learning applications in medical imaging

About Us

CAI2R (pronounced care) comprises approximately 150 full-time personnel dedicated to imaging research, development, and clinical translation. Our team is diverse and highly collaborative. We work in interdisciplinary, matrixed teams that include engineers, scientists, clinicians, technologists, and industry experts.

Joining our team means becoming part of a diverse community that values cross-pollination of ideas, celebrates creativity, and nurtures an environment conducive to breakthrough innovations.

Learn more about our mission, our research, and our team.


The Center for Advanced Imaging Innovation and Research (CAI2R) is supported by the NIH and operated by the department of radiology at NYU Langone Health.

Learn more about our research facilities.

Timeline, Salary, and Benefits

We expect the appointed candidate to start not later than in autumn of 2021. The initial appointment will be for a year, with an intention to renew, depending on mutual agreement. NYU Langone Health offers competitive pay and benefits.

We are committed to diversity and inclusion in all aspects of recruiting and employment. All qualified individuals are encouraged to apply and will receive consideration without regard to race, color, gender, gender identity or expression, sexual orientation, national origin, age, religion, creed, or disability.

To Apply

Email a CV and a short motivation letter to Yvonne Lui, MD, at; Sumit Chopra, PhD, at; and Krzysztof Geras, PhD, at using the phrase “machine learning engineer” in the subject line.