Senior AI and Large Language Model (LLM) Engineer

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.

• →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.

• 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.

• Technically deep in LLM training, augmentation, and evaluation.
• Comfortable influencing architecture across multiple teams.
• Able to move between strategy, design, and implementation.
• Pragmatic and product-oriented.
• Capable of integrating into existing systems and elevating technical execution immediately.
• Experience with life sciences corpora.