Machine Learning Visa Sponsorship Jobs in Maryland
Maryland's machine learning job market is anchored by federal contractors, biotech firms, and cybersecurity companies concentrated around the Washington D.C. corridor, Bethesda, and Baltimore. Major employers including Leidos, Booz Allen Hamilton, and Johns Hopkins APL regularly hire ML engineers and researchers, many of whom require H-1B visa or O-1 visa sponsorship to work in the state.
Find Machine Learning JobsOverview
Showing 5 of 17+ Machine Learning Jobs in Maryland with Visa Sponsorship
See all Machine Learning Jobs in Maryland with Visa Sponsorship
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Jobs in Maryland with Visa Sponsorship.
Get Access To All JobsINTRODUCTION
The Care Health and Reasoning Machines (CHARM) lab led by Dr. Jeremy C. Weiss at the National Library of Medicine seeks to hire a postdoctoral fellow at the intersection(s) of the following research areas: longitudinal machine learning, multi-modal analysis, clinical informatics, and population health.
About the position
The National Library of Medicine (NLM) is one of the 27 institutes at the National Institutes of Health (NIH). NLM is the world’s largest biomedical library and a leader in research, development, and training in biomedical informatics and health information technology. The Division of Intramural Research (DIR) within NLM has two primary research areas: computational health research and computational biology.
-
In computational health research, our efforts center on natural language processing (NLP), clinical image analysis, biomedical ontologies, information modeling, and clinical data analytics.
-
In computational biology, we emphasize transcriptional regulation, chromatin and network biology, structural and functional analysis, sequence statistics, and evolutionary genomics.
The CHARM lab is investigating methods that learn from longitudinal, textual and tabular data present in health data that (1) are predictive forecasters, and (2) preserve interrogable properties. The research will address the trustworthiness of electronic health records and complementary biomedical data for clinical decision making. Relevant experience includes expertise in representation learning, longitudinal data, annotation, curation, large-language models, and multi-modal learning, with applications in health domains. Relevant programming experience includes pytorch/huggingface/sklearn and R/tidyverse.
Recent relevant publications:
- Wang J, Weiss JC. A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports. American Medical Informatics Association Informatics Summit. Pittsburgh, 2025.
- Frattallone-Llado G, Kim J, Cheng C, Salazar D, Edakalavan S, Weiss JC. Using Multimodal Data to Improve Precision of Inpatient Event Timelines. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2024.
- Noroozizadeh S, Weiss JC, and George Chen. “Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression.” Machine Learning for Health. PMLR. 2023.
Position Overview: This is a full-time postdoctoral fellow position. The initial appointment will be for one year, and is renewable on a yearly basis, with extensions up to 5 years total. The NIH offers a competitive stipend (based on postdoctoral experience, see stipend tables) and a stipend supplement for comprehensive health insurance. The NIH is dedicated to the continued education and career development of all its research staff. Candidates are subject to a background investigation. Explore additional information about NIH postdoctoral fellowships.
What you'll need to apply
Applicants should submit the following materials to Jeremy Weiss, MD, PhD at jeremy.weiss@nih.gov:
- Updated CV
- Cover letter with a short research statement and preferred starting date
- Link(s) to published artifacts (packages, github repositories, etc), and
- Contact information for 3 references (please include the full name with titles, institute, email address and phone number of each reference)
Prospective candidates should include "Postdoc Position Inquiry" and their last name in the email subject line.
Application Deadline: Applications will be accepted and reviewed on a rolling basis until the position is filled.
Contact name: Dr. Jeremy Weiss
Contact email: jeremy.weiss@nih.gov
QUALIFICATIONS
- PhD and/or MD, or equivalent in related subject areas, including machine learning, computer science, medicine, bioinformatics, biomedical informatics and statistics/biostatistics
- Clinical Machine Learning: Programming experience in python, pytorch; R, Julia, and/or C++
- Publishing experience with peer-reviewed journals or conferences in the above areas
INTRODUCTION
The Care Health and Reasoning Machines (CHARM) lab led by Dr. Jeremy C. Weiss at the National Library of Medicine seeks to hire a postdoctoral fellow at the intersection(s) of the following research areas: longitudinal machine learning, multi-modal analysis, clinical informatics, and population health.
About the position
The National Library of Medicine (NLM) is one of the 27 institutes at the National Institutes of Health (NIH). NLM is the world’s largest biomedical library and a leader in research, development, and training in biomedical informatics and health information technology. The Division of Intramural Research (DIR) within NLM has two primary research areas: computational health research and computational biology.
-
In computational health research, our efforts center on natural language processing (NLP), clinical image analysis, biomedical ontologies, information modeling, and clinical data analytics.
-
In computational biology, we emphasize transcriptional regulation, chromatin and network biology, structural and functional analysis, sequence statistics, and evolutionary genomics.
The CHARM lab is investigating methods that learn from longitudinal, textual and tabular data present in health data that (1) are predictive forecasters, and (2) preserve interrogable properties. The research will address the trustworthiness of electronic health records and complementary biomedical data for clinical decision making. Relevant experience includes expertise in representation learning, longitudinal data, annotation, curation, large-language models, and multi-modal learning, with applications in health domains. Relevant programming experience includes pytorch/huggingface/sklearn and R/tidyverse.
Recent relevant publications:
- Wang J, Weiss JC. A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports. American Medical Informatics Association Informatics Summit. Pittsburgh, 2025.
- Frattallone-Llado G, Kim J, Cheng C, Salazar D, Edakalavan S, Weiss JC. Using Multimodal Data to Improve Precision of Inpatient Event Timelines. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2024.
- Noroozizadeh S, Weiss JC, and George Chen. “Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression.” Machine Learning for Health. PMLR. 2023.
Position Overview: This is a full-time postdoctoral fellow position. The initial appointment will be for one year, and is renewable on a yearly basis, with extensions up to 5 years total. The NIH offers a competitive stipend (based on postdoctoral experience, see stipend tables) and a stipend supplement for comprehensive health insurance. The NIH is dedicated to the continued education and career development of all its research staff. Candidates are subject to a background investigation. Explore additional information about NIH postdoctoral fellowships.
What you'll need to apply
Applicants should submit the following materials to Jeremy Weiss, MD, PhD at jeremy.weiss@nih.gov:
- Updated CV
- Cover letter with a short research statement and preferred starting date
- Link(s) to published artifacts (packages, github repositories, etc), and
- Contact information for 3 references (please include the full name with titles, institute, email address and phone number of each reference)
Prospective candidates should include "Postdoc Position Inquiry" and their last name in the email subject line.
Application Deadline: Applications will be accepted and reviewed on a rolling basis until the position is filled.
Contact name: Dr. Jeremy Weiss
Contact email: jeremy.weiss@nih.gov
QUALIFICATIONS
- PhD and/or MD, or equivalent in related subject areas, including machine learning, computer science, medicine, bioinformatics, biomedical informatics and statistics/biostatistics
- Clinical Machine Learning: Programming experience in python, pytorch; R, Julia, and/or C++
- Publishing experience with peer-reviewed journals or conferences in the above areas
Machine Learning Job Roles in Maryland
See all Machine Learning Jobs in Maryland
Sign up for free to filter by visa type, set job alerts, and find employers with verified sponsorship history.
Search Machine Learning Jobs in MarylandMachine Learning Jobs in Maryland: Frequently Asked Questions
Which companies sponsor visas for machine learning roles in Maryland?
Federal contractors and defense technology firms are among the most active sponsors in Maryland. Booz Allen Hamilton, Leidos, Northrop Grumman, and SAIC regularly file H-1B petitions for machine learning engineers. On the research and biotech side, Johns Hopkins University, the Johns Hopkins Applied Physics Laboratory, and companies in the Bethesda life sciences corridor also sponsor ML talent. Consulting and cloud firms with Maryland offices, such as Deloitte and Microsoft, are additional sources.
Which visa types are most common for machine learning roles in Maryland?
The H-1B is the most common visa for machine learning professionals in Maryland, as ML engineer and data scientist roles typically qualify as specialty occupations requiring at least a bachelor's degree in a relevant field. Researchers at universities or nonprofit labs may encounter J-1 visa or O-1 visa options. Canadians and Mexicans in qualifying ML roles may be eligible for TN visa status. Those with advanced degrees should also ask employers about EB-2 or EB-1 immigrant pathways.
How to find machine learning visa sponsorship jobs in Maryland?
Migrate Mate is built specifically for international job seekers looking for visa sponsorship roles, including machine learning positions in Maryland. You can filter by state and role type to surface employers who have a documented history of sponsoring work visas. This saves time compared to manually researching which Maryland companies open to sponsorship, particularly among the federal contractors and biotech firms that dominate the state's ML hiring market.
Which cities in Maryland have the most machine learning sponsorship jobs?
The greatest concentration of machine learning sponsorship jobs in Maryland sits in the D.C. suburbs, particularly Bethesda, Rockville, and Silver Spring in Montgomery County, where federal agencies, contractors, and life sciences companies cluster. Baltimore is a secondary hub, driven by Johns Hopkins, the University of Maryland Medical System, and cybersecurity firms. Columbia, located between Baltimore and D.C., also hosts technology and government contractor offices that hire ML professionals.
Are there any Maryland-specific factors that affect machine learning visa sponsorship?
Maryland's heavy reliance on federal contracting creates a notable consideration: many ML roles require a U.S. security clearance, which is generally not available to non-citizens. This limits sponsorship opportunities to roles at commercial, academic, or non-classified government contractor divisions. On the positive side, the proximity to federal research agencies like NIH and NIST in Bethesda means strong demand for ML researchers in healthcare and standards-focused roles, which tend to be more open to international candidates.
What is the prevailing wage for sponsored machine learning jobs in Maryland?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.