Senior AI and Large Language Model (LLM) Engineer
Posted Feb 24, 2026
LOCATION: Bethesda, Maryland (On-site / Not-remote)
Overview
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. Key Responsibilities
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Serve as the AI/LLM subject matter expert across product and engineering teams. - Collaborate with product managers and technical leads to define AI-enabled capabilities and define the use of LLMs across NCBI platforms (e.g., PubMed and related systems). - Develop and implement retrieval-augmented generation (RAG) systems integrating LLMs with large-scale biomedical datasets. - Provide architectural guidance on model selection, domain adaptation, evaluation strategies, and deployment approaches. - Improve model grounding, factual accuracy, and scientific reliability in domain-sensitive applications. - Support engineering teams in productionizing AI solutions, ensuring scalability, performance, and maintainability. - Evaluate emerging LLM techniques and recommend practical adoption strategies aligned with organizational priorities.