Senior AI and Large Language Model (LLM) Engineer

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.

Candidate Experience Requirements

• At least 3+ years of hands-on experience working with LLMs after your last degree
• Only serious candidates accepted — we are not seeking candidates that have recently graduated with a master's degree in the past 1–2 years

Key Responsibilities

• 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

Required Qualifications

• 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)

Preferred Qualifications

• 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

Ideal Candidate Profile

• 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

Benefits

• Medical, dental, and vision coverage
• 401k plan with employer contribution
• Paid holidays and vacation
• Tuition reimbursement
• Competitive salary commensurate with experience and location