Applications Programmer
About The Position
Engaged in the development and automation of AI-driven regulatory science systems and population pharmacokinetics modeling workflows. Responsibilities include assisting with constructing large language models (LLMs) using Retrieval-Augmented Generation (RAG) and benchmarking their performance against leading models such as ChatGPT and Gemini. Support in designing, implementing, and maintaining automated pipelines for model construction, optimization, and result reporting. Additionally, responsible for assisting with developing and managing data sharing and archiving systems to support research projects, ensuring data integrity, accessibility, and reproducibility. Work supports full-cycle system design, implementation, evaluation, maintenance, and documentation. This is an entry-level professional role with limited prior experience, focused on learning to use professional concepts to resolve problems of limited scope and complexity. Employees at this level are expected to acquire the skills and knowledge to perform more advanced work following an agreed-upon time in position, through defined training and development planning. Under general supervision, applies professional computational and data science concepts to the development and automation of AI-driven modeling systems in regulatory science and pharmacokinetics. Assist with projects of moderate scope involving large language model (LLM) construction using RAG frameworks, model performance benchmarking, and workflow automation for population pharmacokinetics modeling. Follows established research and development procedures to collect, analyze, and manage scientific data. Contributes to the design and maintenance of data sharing and archiving systems to ensure research reproducibility and compliance with institutional policies.
Requirements
- Limited prior experience.
- Ability to use professional concepts to resolve problems of limited scope and complexity.
- Ability to acquire skills and knowledge for advanced work through training and development.
- Professional computational and data science concepts.
- Experience with large language model (LLM) construction using RAG frameworks.
- Experience with model performance benchmarking.
- Experience with workflow automation for population pharmacokinetics modeling.
- Ability to follow established research and development procedures.
- Ability to collect, analyze, and manage scientific data.
- Ability to contribute to the design and maintenance of data sharing and archiving systems.
- Understanding of research reproducibility and institutional policies.
Responsibilities
- Development and automation of AI-driven regulatory science systems and population pharmacokinetics modeling workflows.
- Assisting with constructing large language models (LLMs) using Retrieval-Augmented Generation (RAG).
- Benchmarking LLM performance against leading models such as ChatGPT and Gemini.
- Designing, implementing, and maintaining automated pipelines for model construction, optimization, and result reporting.
- Developing and managing data sharing and archiving systems to support research projects.
- Ensuring data integrity, accessibility, and reproducibility.
- Supporting full-cycle system design, implementation, evaluation, maintenance, and documentation.
- Applying professional computational and data science concepts to AI-driven modeling systems.
- Assisting with projects involving LLM construction using RAG frameworks.
- Assisting with model performance benchmarking.
- Assisting with workflow automation for population pharmacokinetics modeling.
- Collecting, analyzing, and managing scientific data following established procedures.
- Contributing to the design and maintenance of data sharing and archiving systems.
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What This Job Offers
Career Level
Entry Level
Education Level
No Education Listed
