MTI is the main product of the Indexing Initiative project and has been providing indexing recommendations based on the MeSH vocabulary since 2002. In 2011, NLM expanded MTI’s role by designating it as the first- line indexer (MTIFL) for a few journals; today the MTIFL workflow includes over 350 journals and continues to increase. The close collaboration of the NLM Index Section, Lister Hill National Center for Biomedical Communications, and Office of Computer & Communications Systems continues to expand and refine the ability of MTI to provide assistance to the indexers.

The ideal applicant must be able to use new technologies and methodologies to address the difficulties associated with medical text indexing and will possess the background and abilities needed to stay current with the rapidly evolving fields of artificial intelligence and machine learning. He/she/they will be essential in maintaining the correctness and efficiency of the current system by optimizing it and providing continuous assistance. The selectee will have the opportunity to participate in trans-NLM and/or trans-NIH projects and committees, serve in outreach- associated or staff-training activities and lead workgroups or teams, such as those that design or influence improvements in program policies, processes, or other key activities. Ideal candidates will work with a diverse group of scientists, bioinformaticians and other developers across the library?to maintain and continually improve the NLM's new machine learning based Medical Text Indexer system (MTIX).?They will leverage cloud-based architectures and technologies to deliver optimized machine learning models at scale.

• Ph.D. in Computer Science, Engineering, Physics, or?Applied Mathematics. • 15+ years of relevant computer programming experience in a Windows or Linux environment. • 6+ years of on-the-job experience with an industry recognized machine learning framework (e.g., PyTorch or?TensorFlow). • Proficiency in the?Python programming language. • Expert in deep learning for natural language processing. • Knowledge?and?experience of Large Language Models (LLMs) for natural language processing?(e.g., BERT). • Proven track record developing machine learning systems to solve real-world problems. • Experience of the full machine learning project lifecycle including dataset creation, model training, model evaluation, and model deployment. • Experience of neural network training and inference using?high-performance computing resources (e.g., NIH BioWulf). • Experience deploying machine learning models to cloud computing environments (e.g., using Amazon Sagemaker). • Knowledge of controlled vocabularies (e.g., MeSH) and their application to document indexing (e.g., MEDLINE indexing). Desired Competencies: • Experience working on?document processing systems. • Experience of the Hugging Face Transformers Python package. • Experience working in an Agile software development team. • Knowledge and experience?of modern software development methodologies (e.g., test driven development). • Working knowledge of?modern software development tools;?including version control (e.g., Git), build management and continuous integration (e.g., TeamCity), and issue tracking (e.g., JIRA). • Experience of automated software testing (writing unit and integration tests). The successful applicant must have demonstrated the necessary skills and abilities to lead, direct, organize, and coordinate complex research and development projects and will have exceptional technical competence. Candidates must be able to work independently, have excellent communication skills and a proven ability to successfully engage with others to create useful resources to achieve shared objectives.

The position of Staff Scientist 1 is located in IEB’s Literature Development Team. This team is responsible for all Literature related databases and programs at the NCBI. These include PubMed, an index of life sciences journal literature of over 35 million records, PubMed Central (PMC), a free full-text digital archive of life sciences journal articles, and the NCBI Bookshelf, a repository for non-journal literature. Additionally, the Literature Team supports the NIH Public Access policy and the NIH Manuscript Submission system, which prepares manuscripts resulting from NIH-funded research for inclusion in PMC. This program integrates the deposited literature with other NCBI resources, such as GenBank, and provides facilities for accessing the deposited literature and links to related resources.

Selectees who have completed part or all their education outside of the United States must have their foreign education evaluated by an accredited organization to ensure that the foreign education is equivalent to education received in accredited educational institutions in the United States. We will only accept the completed foreign education evaluation. For more information on foreign education verification, visit the National Association of Credential Evaluation Services (NACES) website. Verification must be received prior to the effective date of the appointment.

Benefits:

Salary is commensurate with research experience and accomplishments. A full package of benefits, including retirement, health, life, and long-term care insurance, Thrift Savings Plan participation, etc., is available.

The successful candidate will serve in a non- competitive, time-limited, renewable appointment in the excepted service.

DHHS, NIH and NLM are Equal Opportunity Employers The NIH is dedicated to building a diverse community in its training and employment programs. Applications from women, persons from underrepresented groups, and persons with disabilities are strongly encouraged.

Commitment to Diversity and Equal Employment Opportunity NIH/NLM encourages the application and nomination of qualified women, minorities, and individuals with disabilities. The U.S. government does not discriminate in employment based on race, color, religion, sex (including sexual orientation, pregnancy, and gender identity), national origin, political affiliation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factors. NIH/NLM will provide reasonable accommodations to applicants with disabilities as appropriate. If you require reasonable accommodation during any part of the application and hiring process, please notify us.