AI Computational Engineer (JR230420)

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Yonkers, New York

Montefiore

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keywords: position summary,position details,develop,technical,quality assurance,evaluate,support,research,education & experience,skills,preferred,additional preferred skills

Full time

$124,000.00-$155,000.00

Overview: <p>The 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.</p>
Responsibilities: <ul> <li>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.</li> <li>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.</li> <li>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.</li> <li>Turn validated methods into maintainable research services and interfaces, with version control, automated tests, monitoring, user documentation, and clear handoff to authorized operational partners.</li> <li>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.</li> <li>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.</li> <li>Support investigators through consultations, demonstrations, training materials, and reusable examples; contribute technical content to publications, grants, and multi-site research collaborations.</li> <li>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.</li> </ul>
Requirements: <ul> <li>Bachelor&rsquo;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&rsquo;s degree may provide that experience.</li> <li>Strong Python programming skills and practical experience with a modern machine learning framework such as PyTorch or TensorFlow.</li> <li>Demonstrated ability to prepare complex datasets, run controlled experiments, assess model performance, and communicate limitations clearly.</li> <li>Experience using Git and working collaboratively on documented, maintainable software or research pipelines.</li> <li>Ability to translate needs across technical and nontechnical colleagues, prioritize practical work, and learn biomedical and research operations concepts.</li> </ul> <br /> <p><strong>Preferred:</strong></p> <ul> <li>Master&rsquo;s degree or equivalent research or industry experience in AI, computer science, biomedical informatics, or a related field, or equivalent experience.</li> <li>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.</li> <li>Experience with GPU or high-performance computing, reproducible model training and inference, containers, SQL, APIs, or research data platforms.</li> <li>Familiarity with clinical research workflows, FAIR data practices, common data models, biomedical ontologies, data provenance, human review, or responsible AI assessment.</li> <li>Evidence of research communication and user focused delivery through publications, presentations, open-source work, training, or documented deployed projects.</li> </ul>