AI/Computational Biologist

Goldbelt, Inc.•Frederick, MD

About The Position

The AI/Computational Biologist will serve as a subject matter expert in applied Artificial Intelligence (AI), leveraging Large Language Models (LLMs) and Machine Learning (ML) to accelerate early-stage medical countermeasure discovery and testing as well as to support late-stage product development efforts. Provide team AI Enablement and technical consultation, act as a technical translator and force multiplier for the team, bridging the gap between advanced AI capabilities and practical scientific application, provides hands-on support to other scientists, demystifying complex AI tools, and identifying opportunities to integrate AI and automation into existing research workflows.

Requirements

Five (5) years of working experience with Microsoft Office tools (Outlook, Project, PowerPoint, Excel, Teams, and Word).
Research and review scientific literature and databases to identify technical information to support product development.
Effectively communicate results of assigned tasks and efforts, both orally and in written form.
Receive and review debriefs at the Secret level on Biannual Threat Briefs and be able to assist on course of action development in support of IPT's.
Master's or PhD in Molecular, Computational or Structural Biology, Bioinformatics, Biomedical Engineering, Biophysics/Computational Chemistry, Pharmaceutical Science, Chemistry, Immunology, or a related discipline.
5-12 years of experience with AI technical implementation, computational tools, and/or troubleshooting
Secret Clearance

Responsibilities

Demonstrated AI experience through publications, a coding/development portfolio (e.g., GitHub examples), or other relevant examples documenting experience applying AI/Computational Biology to complex biological problems.
Strong understanding of relevant AI tools and the ability to debate highly technical and implementation issues.
Ability to translate complex scientific and technical data into acquisition relevant programmatic recommendations.
Expert in analyzing structural biology and multi-omics (e.g., genomics, proteomics) data, provide deep biological context to AI-driven findings, generate mechanistic hypotheses for novel drug targets, and validating the biological plausibility of computational models, to strengthen the scientific foundation of discovery projects.