Flagship Labs 104 (FL104) is looking to transform how we reason about complex phenomena by leveraging artificial intelligence and large language models (LLMs) to drive breakthroughs in drug discovery and fundamental science.
Requirements
- Expertise in QSP, PK/PD modeling, and simulation tools such as MATLAB, Monolix, SimBiology, or Julia.
- Experience building and simulating mechanistic models in Python environments.
- Familiarity with hybrid modeling approaches that combine mechanistic models and machine learning.
- Experience with Git, AWS, and collaborating on software projects.
Responsibilities
- Lead the design and implementation of quantitative systems pharmacology (QSP), physiology-based pharmacokinetics (PBPK), pharmacokinetics/pharmacodynamics (PKPD), and translational mechanistic models for disease biology and therapeutic response.
- Develop and apply modeling frameworks to prioritize targets, optimize drug properties, and inform clinical trial design.
- Beta test and benchmark FL104’s software to identify and prioritize opportunities to improve performance and user experience and provide feedback to AI researchers and engineers.
Other
- PhD. in systems pharmacology, bioengineering, applied mathematics, or a related field, with 12+ years of industry experience.
- Proven track record in developing and applying mechanistic models in translational or clinical contexts.
- Experience managing alliances with multi-disciplinary teams in pharma, biotech, or academic translational research settings.
- Strong communication skills and the ability to translate complex models into actionable insights for diverse stakeholders.
- Experience contributing to IND-enabling or clinical development programs and leading computational modeling within programs.
- Background in immunology, oncology, or metabolic disease is a plus.
