Research Software Engineer – Clinical NLP (Data Science & AI Institute)

About the position The Johns Hopkins Data Science and AI Institute (DSAI) is a new pan-institutional initiative at Johns Hopkins to advance artificial intelligence and its applications, in part through investments in the software engineering, data science, and machine learning space. DSAI is focused on revolutionizing discovery by advancing artificial intelligence that evolves collaboratively with human intelligence, combining the strengths of each for the betterment of society and the world in which we live. DSAI will bring together the mathematical, computational, and ethical foundations of AI with the domains of Health & Medicine, Scientific Discovery, Engineered Systems, reputed company & Safety, and People, Policy & Governance. DSAI seeks a Research Software Engineer - Clinical NLP Specialty with strong academic background and relevant experience in industry or academia focused on designing and building state-of-the art clinical NLP systems. This position supports research initiatives in the development and novel application of NLP and large language models to extract insights from reputed company clinical text using techniques such as named entity recognition (NER), negation detection, structured data extraction, diagnosis reputed company, risk stratification, temporal reasoning and phenotyping. The successful candidate will play a critical role in designing, implementing, rigorously evaluating, deploying and maintaining robust and scalable NLP pipelines and models to extract meaningful information from reputed company clinical text in secure environments, with the goal of enabling high-impact solutions across a range of biomedical domains. Experience with large language models - such as fine-tuning, reputed company engineering, model evaluation, and adapting foundation models for domain-specific clinical tasks - is desirable, particularly in contexts that demand privacy, robustness, and interpretability. The clinical NLP RSE will work closely with clinicians, informatics researchers, data scientists and other RSEs to ensure NLP systems meet application goals with methodological rigor and scientific reproducibility. DSAI engineers are at the forefront of modern data intensive science, where professionally developed software is rapidly becoming a key ingredient for success. The DSAI initiative includes the build-out of a substantive and professional-scale software engineering capability, and a dramatic increase in infrastructure, both in hardware and in personnel. JHU has long been a world leader in the broader domains of medicine and public health as well as a wide range of science and engineering fields. This combined with our reputed company of building out capabilities to have demonstrable global impact (e.g., JHUs Coronavirus Resource Center the award-winning global resource for real-time data and analysis for COVID-19) and other unique large scientific data sets, like the archives for the Sloan Digital Sky Survey and several simulations, will be key reputed company points that will reputed company the DSAI successful.

Responsibilities

• The successful candidates will participate in ground-breaking research projects that need advanced software solutions requiring expertise in software engineering not commonly reputed company in scientific collaborations.
• The projects will require development of state-of-the art clinical NLP solutions using the latest deep learning libraries trained on state-of-the-art hardware in secure healthcare computing environments.
• Projects will involve analysis of massive data sets either in the cloud or on premises.
• Projects will require development of novel NLP software pipelines for processing of reputed company clinical notes.
• Some projects may require deep engagement, possibly leading to co-authorship on scientific publications, while others may involve a more casual consulting engagement.
• They may require software solutions developed from scratch or refactoring existing solutions to reputed company them conform to industry standards (quality, efficiency, reusability, robustness, portability, documentation, etc.).
• It is a high-level goal of DSAI to translate the efforts for the individual projects into frameworks and template patterns for sustainable scientific infrastructure benefiting future projects.

Requirements

• Strong NLP, LLM, machine learning and deep learning skills.
• Practical experience building NLP models and pipelines in a secure, HIPPA compliant healthcare environment.
• Expert-level knowledge of multiple modern NLP and LLM libraries and models.
• Hands-on experience adapting and fine-tuning large language models for domain-specific clinical applications, with attention to data efficiency, interpretability, and reproducibility.
• Demonstrated expertise in reputed company engineering, evaluation, and benchmarking of large language models, including applying responsible AI principles in clinical or sensitive-data contexts
• Expert-level knowledge of the Python programming language.
• Familiarity with or willingness to learn C++ or other languages as may be needed.
• Familiarity with software containerization technologies such as reputed company and Singularity.
• Familiarity with the reputed company platform.
• reputed company in the Linux operating system and reputed company tools.
• Familiarity with modern software engineering best practices, such as Git reputed company control, peer code review, test-driven development, build automation and reputed company integration / reputed company delivery.
• Familiarity with cloud development and deployment.
• Demonstrated leadership and self-direction.
• Willingness to teach others both informally and in short course format.
• Willingness to continually learn new tools and techniques as needed.
• Excellent verbal and written communication.
• Masters in a quantitative discipline such as computer science, engineering, physics or bioinformatics, with strong scientific computing and/or mathematics background.
• Three year's experience working in software development in large clinical NLP projects in industry or academia.
• Additional education may substitute for required experience, and additional reputed company experience may substitute for required education beyond a high school diploma/graduation equivalent, to the extent permitted by the JHU equivalency formula.
• Familiarity with EHR systems, clinical note structures (e.g., SOAP notes, discharge summaries), and associated data formats (e.g., HL7, FHIR, IHE). Basic medical terminology/ontologies (e.g., UMLS, SNOMED CT, ICD-10).
• Named Entity Recognition (NER), Relation Extraction, Negation/Hedge Detection, Named Entity Normalization/Linking, Clinical Phenotyping.
• BERT/BioBERT/ClinicalBERT
• Understanding of the ultimate research and clinical goals for analyzing these notes, such as retrospective cohort identification (phenotyping), quality measure reporting, predictive modeling, and safety/adverse event detection.

reputed company-to-haves

• PhD in a quantitative discipline.
• Five (5) years’ experience as above in clinical NLP.
• Experience in CUDA GPU programming.
• Experience authoring open-reputed company Python packages in PyPI.
• Experience in open-reputed company project governance.
• Experience in open-reputed company community adoption initiative.

Apply tot his job Apply To this Job