Senior Research Engineer

Senior 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.

Position Summary:

We have an exciting opportunity to join our team as a Senior Research Engineer. The newly established NYU Langone Center for Orthopedic Data Science and Artificial Intelligence (CODA) is seeking a Senior Machine Learning Research Engineer to develop next-generation multimodal ML systems for musculoskeletal care. This engineer will be our first engineering hire and responsible for architectural and modeling groundwork for how we curate, model, and utilize highly unique, multimodal clinical datasets (e.g. radiographic imaging, clinical photographs, clinical videos, natural language and electronic health records).

Working closely with a multidisciplinary team, you will translate complex real-world challenges into robust ML solutions and research workflows as part of an integrated bedside to bench and back approach. This is a foundational hire, with the candidate shaping our technical direction from scratch, but with the full backing of NYU Langone's data and compute infrastructure. The ideal candidate will combine technical depth with excellent cross-disciplinary collaboration skills, clear communication, and the ability to navigate open-ended scientific problems with curiosity and rigor.

Clinical data is primarily managed by NYU Langone's internal data team, MCIT, which will form the structured foundation for these efforts. Significant computational resources are available via our institutional high-performance supercomputing cluster. Numerous collaborating labs are available to provide infrastructure and experience. You will work directly with Dr. Jie Yao as a respected collaborator to drive the centers technical direction.

Job Responsibilities:

• End-to-End ML Development:
  Own the full lifecycle of our early AI initiatives. You will architect data pipelines to ingest complex clinical data, train foundational machine learning models, and establish the infrastructure to securely deploy and monitor these systems.
• Research:
  Define critical quality improvement and research questions; and contribute to fundamental method development including statistical, machine learning, and optimization-based approaches. Pursue and co-author publishable research in collaboration with clinical and scientific partners.
• Technical Foundation:
  Establish the centers engineering standards. Help define best practices for code quality, implement version control, and make core architectural decisions regarding our technology stack and compute infrastructure. These early decisions will lay the groundwork for how CODA builds moving forward.
• Clinical Translation:
  Serve as a bridge between machine learning, clinical practice, and scientific research. Work closely with surgeons, biologists, engineers, and clinical researchers to translate ambiguous clinical workflows and research goals into concrete technical problems. Communicate model capabilities, trade-offs, uncertainty, and data limitations clearly to collaborators from diverse backgrounds while ensuring solutions remain clinically relevant, interpretable, and practical.
• Team Development:
  Support recruiting as the center grows. Contribute to continuing education and professional development including conferences, journal clubs, and other educational activities. Help shape a collaborative culture.

Minimum Qualifications:

To qualify you must have a

Education:

B.S./M.S. in Computer Science, Data Science, or related quantitative fields with 3+ years of industry or equivalent ML experience; OR a PhD in a related field (including dissertation work). Technical Proficiency:
Strong programing skills in Python and SQL with experience working with large relational datasets (e.g. cohort construction, longitudinal analysis, or feature engineering from production or clinical databases). Expertise in modern deep learning frameworks (e.g. PyTorch and Tensor Flow) and standard data processing libraries, and familiarity with containerization (e.g. Docker) and computing infrastructure. Systems Architecture:
Proven industry experience or a strong research track record demonstrating ability to build end-of-to-end experimental pipelines, handle large, unstructured datasets, and rigorously evaluate model performance. Cross-Domain Communication:
Exceptional…