Senior Research Engineer

NYU Langone Health•New York, NY

3d•$101,494 - $132,088•Onsite

About The Position

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.

Requirements

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).
Strong programming 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 TensorFlow) and standard data processing libraries, and familiarity with containerization (e.g. Docker) and computing infrastructure.
Proven industry experience or a strong research track record demonstrating ability to build end-to-end experimental pipelines, handle large, unstructured datasets, and rigorously evaluate model performance.
Exceptional collaboration and communication skills, including the ability to work effectively with clinicians, surgeons, biologists, and researchers from diverse technical backgrounds.
Demonstrated ability to explain complex algorithmic trade-offs, uncertainty, modeling decisions, and data limitations to collaborators across domains.
Excellent communication skills with proficiency in written and oral English.
A proven ability to drive open-ended projects from initial concept to a completed, reproducible, and scalable solution.
Qualified candidates must be able to effectively communicate with all levels of the organization.

Nice To Haves

Ph.D. that includes 3+ years, including dissertation work in machine learning, computer vision, natural language processing, or a related area.
Experience with medical imaging (XR, MRI, CT) or multimodal clinical datasets.
Familiarity with clinical data standards (HL7, FHIR, DICOM).
Experience working in a regulated or HIPAA-compliant environment.
A previous publication record in ML, clinical AI, or a related field.
Experience with high performance computing systems.

Responsibilities

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.
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.
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.
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.
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.