Senior AI and Large Language Model (LLM) Engineer
Bethesda, MarylandAll locationsBethesda, MarylandNIH-Bethesda On-site
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About the role
We are seeking an experienced AI/LLM Engineer to lead the design, customization, and integration of large language models (LLMs) into biomedical research workflows and information retrieval systems. We are looking for someone with hands-on experience training, fine-tuning, augmenting, and deploying LLMs in production environments, ideally within biomedical or life sciences domains. The role is product-oriented and forward-looking, focused on building the next generation of AI-enabled search, retrieval, and knowledge discovery tools. This role serves as a subject matter expert (SME) across multiple product and engineering teams. The selected candidate will help define, architect, and implement LLM-driven capabilities across a portfolio of NCBI services. The position requires strong technical depth, sound architectural judgment, and the ability to collaborate effectively within existing product and technical ecosystems. This is a hands-on, build-oriented role with strategic influence. The candidate must be capable of guiding both what gets built and how it gets built. Only serious candidates accepted - we are not seeking candidates that have recently graduated with a masters degree in the past 1-2 years . We are seeking candidates with at least 3+ years experience doing this work after your last degree.
What you'll bring
- 3+ years of hands-on experience working with large language models (training, fine-tuning, augmentation, or deployment).
- Demonstrated experience integrating LLMs into production systems (e.g., semantic search, RAG pipelines, domain-specific QA).
- Strong experience in ML system architecture and scalable deployment.
- Proven ability to work cross-functionally with product and technical teams.
- Experience serving as a technical SME guiding multi-team initiatives.
- Strong programming skills in Python.
- Experience with modern ML frameworks (e.g., PyTorch, Hugging Face) and retrieval infrastructure (e.g., embeddings, vector databases).
- Experience building LLM-based systems for biomedical research or life sciences.
- Familiarity with large scientific corpora, biomedical ontologies, structured knowledge bases, or biological datasets.
- Experience developing generative AI systems for DNA, RNA, or protein sequence analysis.
- Background in bioinformatics, computational biology, or related disciplines.
- Experience improving factual grounding and reducing hallucinations in scientific or regulated environments.