AI Computational Engineer

Montefiore Einstein; City of Yonkers (NY); On-site; USD 124,000 - 155,000; Full time

Job summary

Montefiore Einstein in Yonkers, NY seeks an AI Computational Engineer to advance Health Research Informatics by building data and AI capabilities that support investigators from data access to reproducible results.

You will develop data pipelines, implement governed workflows, and translate researcher needs into technical milestones while collaborating with ICTR and security teams.

Qualifications

• Bachelor’s degree in computer science, data science, or a related quantitative field.
• Experience building ML or data-intensive software.
• Strong Python and ML framework experience (PyTorch or TensorFlow).
• Experience with Git and collaborative software pipelines.
• Ability to translate investigator needs to actionable requirements.

Responsibilities

• Develop and maintain research data pipelines integrating clinical records, text, imaging, and other sources.
• Document data definitions, quality checks, metadata, lineage, and transformations.
• Implement governed data access workflows and cohort discovery tools.
• Develop, benchmark, and validate AI methods including multimodal models.
• Turn validated methods into maintainable services with tests and docs.
• Collaborate with ICTR, Data Science Institute, and governance partners.
• Track service measures and report progress.

Skills

Python

PyTorch/TensorFlow

Machine Learning

Git

Data Pipelines

Communication of results

Education

Bachelor’s degree in Computer Science or related field

Tools

SQL

Docker

Job description

City/State:Yonkers, New YorkGrant Funded:NoDepartment:IT - Data & Analytics Health Research InformaticsWork Shift:DayWork Days:MON-FRIScheduled Hours:8:30 AM-5 PMScheduled Daily Hours:7.5 HOURSPay Range:$124,000.00-$155,000.00The AI Computational Engineer will work on the operational development of Health Research Informatics at Montefiore Einstein, building dependable data and AI capabilities that help investigators move from a research question to governed data access, cohort discovery, study feasibility, analysis, and reproducible results. The role combines hands on computer science research with implementation of reusable services. This individual may focus on different data types or methods while sharing responsibility for rigorous evaluation, documentation, and investigator support.Key Responsibilities:Develop and maintain research data pipelines that integrate clinical records, text, imaging, and other approved data sources. Document data definitions, quality checks, metadata, lineage, and transformations to support FAIR aligned reuse.Help implement standardized, governed data access workflows and reusable tools for cohort discovery, phenotyping, trial feasibility, recruitment planning, and research analytics. Translate investigator needs into scoped requirements and track delivery.Develop, benchmark, and validate AI methods, including information extraction, multimodal models, large or vision language models, and interpretable or knowledge informed approaches when suitable for the use case.Turn validated methods into maintainable research services and interfaces, with version control, automated tests, monitoring, user documentation, and clear handoff to authorized operational partners.Set evaluation plans with investigators and clinical experts, including baselines, validation splits, human review, error analysis, reproducibility checks, and assessment of subgroup performance and potential bias.Work with ICTR, the Data Science Institute, and Montefiore data, IT, security, privacy, and oversight partners to implement approved access and governance requirements. Prepare technical evidence for review and escalate higher risk use cases through established channels.Support investigators through consultations, demonstrations, training materials, and reusable examples; contribute technical content to publications, grants, and multi-site research collaborations.Track service measures such as request turnaround, cohort or feasibility output quality, reuse of methods, model performance, and investigator feedback; report progress and operational issues to the CRIO.Qualifications:Required:· Bachelor’s degree in computer science, computer engineering, data science, or a closely related quantitative field, plus substantive experience building machine learning or data intensive software; a master’s degree may provide that experience.· Strong Python programming skills and practical experience with a modern machine learning framework such as PyTorch or TensorFlow.· Demonstrated ability to prepare complex datasets, run controlled experiments, assess model performance, and communicate limitations clearly.· Experience using Git and working collaboratively on documented, maintainable software or research pipelines.· Ability to translate needs across technical and nontechnical colleagues, prioritize practical work, and learn biomedical and research operations conceptsPreferred:Master’s degree or equivalent research or industry experience in AI, computer science, biomedical informatics, or a related field, or equivalent experience.Experience in one or more of the following: medical imaging; clinical NLP or information extraction; large language or vision language models; multimodal learning; clinical or other sensitive research data.Experience with GPU or high-performance computing, reproducible model training and inference, containers, SQL, APIs, or research data platforms.Familiarity with clinical research workflows, FAIR data practices, common data models, biomedical ontologies, data provenance, human review, or responsible AI assessment.Evidence of research communication and user focused delivery through publications, presentations, open-source work, training, or documented deployed projects.Montefiore Health System, Inc. is an equal employment opportunity employer. Montefiore Health System, Inc. will recruit, hire, train, transfer, promote, layoff and discharge associates in all job classifications without regard to their race, color, religion, creed, national origin, alienage or citizenship status, age, gender, actual or presumed disability, history of disability, sexual orientation, gender identity, gender expression, genetic predisposition or carrier status, pregnancy, military status, marital status, or partnership status, or any other characteristic protected by law.