The Johns Hopkins University OPT Eligible Jobs USA
Johns Hopkins University actively hires F-1 students on OPT across research, clinical, engineering, and administrative roles. As a major research institution, it's a strong destination for OPT students in STEM and healthcare fields who want hands-on experience with a clear path toward long-term U.S. employment.
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Get Access To All JobsINTRODUCTION
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, Security & 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 unstructured clinical text using techniques such as named entity recognition (NER), negation detection, structured data extraction, diagnosis prediction, 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 unstructured 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, prompt 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 ethos 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 leverage points that will make the DSAI successful.
SPECIFIC DUTIES & RESPONSIBILITIES
- The successful candidates will participate in ground-breaking research projects that need advanced software solutions requiring expertise in software engineering not commonly found 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 unstructured 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 make 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.
SPECIAL KNOWLEDGE, SKILLS, AND ABILITIES
- 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 prompt 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 Docker and Singularity.
- Familiarity with the Databricks platform.
- Fluency in the Linux operating system and related tools.
- Familiarity with modern software engineering best practices, such as Git source control, peer code review, test-driven development, build automation and continuous integration / continuous 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.
MINIMUM QUALIFICATIONS
- Masters in a quantitative discipline such as computer science, engineering, physics or bioinformatics, with strong scientific computing and/or mathematics background.
- Three years experience working in software development in large clinical NLP projects in industry or academia.
- Additional education may substitute for required experience, and additional related experience may substitute for required education beyond a high school diploma/graduation equivalent, to the extent permitted by the JHU equivalency formula.
PREFERRED QUALIFICATIONS
- PhD in a quantitative discipline.
- Five (5) years’ experience as above in clinical NLP.
- Experience in CUDA GPU programming.
- Experience authoring open-source Python packages in PyPI.
- Experience in open-source project governance.
- Experience in open-source community adoption initiative.
Clinical Data Expertise
- 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).
Core Clinical NLP Tasks & Tools
- Named Entity Recognition (NER), Relation Extraction, Negation/Hedge Detection, Named Entity Normalization/Linking, Clinical Phenotyping.
Deep Learning Tools
- BERT/BioBERT/ClinicalBERT
Data Science for Clinical Studies
- 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.
Classified Title: Scientific Software Engineer
Job Posting Title (Working Title): Research Software Engineer – Clinical NLP (Data Science & AI Institute)
Role/Level/Range: APPTSTAF/01/ST
Starting Salary Range: Commensurate w/exp.
Employee group: Full Time
Schedule: M-F, 37.5 hrs/wk
FLSA Status: Exempt
LOCATION
Hybrid/Mount Washington Campus
Department name: DSAI Institute
Personnel area: Whiting School of Engineering
INTRODUCTION
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, Security & 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 unstructured clinical text using techniques such as named entity recognition (NER), negation detection, structured data extraction, diagnosis prediction, 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 unstructured 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, prompt 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 ethos 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 leverage points that will make the DSAI successful.
SPECIFIC DUTIES & RESPONSIBILITIES
- The successful candidates will participate in ground-breaking research projects that need advanced software solutions requiring expertise in software engineering not commonly found 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 unstructured 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 make 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.
SPECIAL KNOWLEDGE, SKILLS, AND ABILITIES
- 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 prompt 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 Docker and Singularity.
- Familiarity with the Databricks platform.
- Fluency in the Linux operating system and related tools.
- Familiarity with modern software engineering best practices, such as Git source control, peer code review, test-driven development, build automation and continuous integration / continuous 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.
MINIMUM QUALIFICATIONS
- Masters in a quantitative discipline such as computer science, engineering, physics or bioinformatics, with strong scientific computing and/or mathematics background.
- Three years experience working in software development in large clinical NLP projects in industry or academia.
- Additional education may substitute for required experience, and additional related experience may substitute for required education beyond a high school diploma/graduation equivalent, to the extent permitted by the JHU equivalency formula.
PREFERRED QUALIFICATIONS
- PhD in a quantitative discipline.
- Five (5) years’ experience as above in clinical NLP.
- Experience in CUDA GPU programming.
- Experience authoring open-source Python packages in PyPI.
- Experience in open-source project governance.
- Experience in open-source community adoption initiative.
Clinical Data Expertise
- 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).
Core Clinical NLP Tasks & Tools
- Named Entity Recognition (NER), Relation Extraction, Negation/Hedge Detection, Named Entity Normalization/Linking, Clinical Phenotyping.
Deep Learning Tools
- BERT/BioBERT/ClinicalBERT
Data Science for Clinical Studies
- 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.
Classified Title: Scientific Software Engineer
Job Posting Title (Working Title): Research Software Engineer – Clinical NLP (Data Science & AI Institute)
Role/Level/Range: APPTSTAF/01/ST
Starting Salary Range: Commensurate w/exp.
Employee group: Full Time
Schedule: M-F, 37.5 hrs/wk
FLSA Status: Exempt
LOCATION
Hybrid/Mount Washington Campus
Department name: DSAI Institute
Personnel area: Whiting School of Engineering
Job Roles at The Johns Hopkins University
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Get Access To All JobsTips for Finding The Johns Hopkins University OPT Eligible Jobs USA
Target research and clinical departments directly
Johns Hopkins' strongest OPT hiring happens inside its research centers and medical campus divisions, not always through central HR postings. Contact labs and clinical departments directly, as hiring managers there often move faster than university-wide postings suggest.
Confirm E-Verify enrollment before accepting offers
If you're pursuing a STEM OPT extension, your employer must be enrolled in E-Verify before your first day. Johns Hopkins as an institution is enrolled, but verify that the specific entity or subsidiary making your offer is covered before signing anything.
Align your OPT start date with your offer timeline
USCIS grants your OPT EAD with a fixed start date you choose when applying. Coordinate with Johns Hopkins' HR team early so your authorized start date matches your onboarding date. A mismatch can delay your first paycheck legally.
Use Migrate Mate to research Johns Hopkins' H-1B filing history
Before accepting an OPT role, check whether Johns Hopkins has a track record of sponsoring H-1B for employees in your job category. Migrate Mate surfaces DOL Labor Condition Application data by employer and role so you can evaluate long-term sponsorship likelihood before you commit.
Match your degree field to your OPT job description
Johns Hopkins HR and international offices will scrutinize whether your role directly relates to your degree. Use O*NET to identify the standard occupational description for your role and make sure your job offer letter reflects that language clearly.
Request a written confirmation of STEM OPT eligibility early
Not every role at Johns Hopkins qualifies for the 24-month STEM OPT extension. Ask your hiring manager and DSO to confirm the role's SOC code and STEM designation in writing before your initial OPT period ends, giving yourself time to switch roles if needed.
The Johns Hopkins University OPT Eligibility: Frequently Asked Questions
Does The Johns Hopkins University sponsor OPT visas?
Johns Hopkins doesn't sponsor OPT directly. OPT is work authorization USCIS grants to F-1 students, not a visa employers file for. What Johns Hopkins does is hire students who already hold a valid OPT EAD. The university is enrolled in E-Verify, which is required if you plan to pursue a STEM OPT extension after your initial 12-month period.
Which departments at Johns Hopkins typically hire OPT students?
Research labs, the Bloomberg School of Public Health, the School of Medicine, the Whiting School of Engineering, and the Applied Physics Laboratory are among the most active hiring areas for OPT students. These departments regularly bring on graduate-level talent in STEM, biomedical, and data-focused roles that align well with OPT work authorization requirements.
How do I navigate the OPT hiring process at Johns Hopkins?
Once you receive an offer, provide your OPT EAD to Johns Hopkins HR and complete the I-9 verification. Your DSO at your home institution handles any required SEVIS updates. Johns Hopkins HR coordinates the E-Verify check on their end. Keep your EAD expiration date in mind and begin STEM OPT extension paperwork at least 90 days before it expires.
How long does the OPT hiring and onboarding process take at Johns Hopkins?
Once your OPT EAD is approved by USCIS, the onboarding process at Johns Hopkins itself is typically straightforward. USCIS recommends applying for OPT at least 90 days before your intended start date since processing can take several months. Coordinate your EAD start date with your offer timeline to avoid gaps between your first day and your authorization period.
Does Johns Hopkins sponsor H-1B after OPT ends?
Johns Hopkins has a history of sponsoring H-1B visa for employees in research, clinical, technical, and administrative roles. OPT is often a direct pathway into H-1B sponsorship there, but it depends on your department and role. Use Migrate Mate to review Johns Hopkins' LCA filing history by job category so you can assess sponsorship likelihood for your specific position before your OPT period ends.
How does The Johns Hopkins University hire OPT students?
OPT is work authorization granted directly to F-1 students after graduation — no employer petition is required. The Johns Hopkins University can hire OPT students as soon as their EAD card is approved. STEM degree holders can extend OPT by 24 months when their employer is enrolled in E-Verify. Most companies that hire OPT students also support the transition to H-1B when the student's OPT period is ending. Check The Johns Hopkins University's individual postings on Migrate Mate to confirm OPT acceptance per role.