AI-Powered Regulatory IRB Review Platform
Budget: $15 – $25 USD
We are developing an AI-powered platform designed to streamline the IRB review process by providing regulatory guidance, document analysis, and decision support to IRB analysts. This system is being built from the ground up as part of a new NIH SBIR Phase I initiative (pending funding) to modernize and simplify human research compliance. By accelerating and improving the ethics review process, this platform aims to save lives by advancing research more efficiently and helping to transform the broader research ecosystem for the better.
Important Note:
If you are selected as a strong candidate, I will ask you to complete a brief NIH biosketch as part of our SBIR Phase I application. This does not obligate you to join the team, but it is required for submission. We will reconnect in December 2025 to confirm participation if funding is awarded. The start date of this position would be early 2026. The time commitment is approximately 620 hours over the span of 3-6 months.
Role Overview
The Data Architect will lead the design and implementation of a secure, scalable, and organized data infrastructure to support all platform features, including document storage, AI feedback integration, user data, and regulatory mapping. This is a critical role that will ensure the integrity, interoperability, and accessibility of data across the IRB Assist ecosystem.
Key Responsibilities
Design the backend data architecture for the IRB Assist platform, including data models, schemas, and APIs.
Define how documents, feedback, user interactions, and metadata are stored, retrieved, and secured.
Ensure data privacy in all system designs.
Collaborate with Full Stack Developers and AI/NLP Engineers to integrate the data system with user interfaces and AI components.
Document architecture choices and support long-term scalability and maintainability.
Ideal Candidate
5+ years of experience in data architecture, data engineering, or backend systems design.
Strong background in designing secure, distributed systems with relational and non-relational databases.
Experience working with protected health information (PHI), HIPAA-compliant systems, or research data preferred.
Familiarity with AWS, Azure, or other cloud-based data infrastructure tools.
Comfortable working in a startup-like, early-stage environment with limited resources and high impact.
Important Note:
If you are selected as a strong candidate, I will ask you to complete a brief NIH biosketch as part of our SBIR Phase I application. This does not obligate you to join the team, but it is required for submission. We will reconnect in December 2025 to confirm participation if funding is awarded. The start date of this position would be early 2026. The time commitment is approximately 620 hours over the span of 3-6 months.
Role Overview
The Data Architect will lead the design and implementation of a secure, scalable, and organized data infrastructure to support all platform features, including document storage, AI feedback integration, user data, and regulatory mapping. This is a critical role that will ensure the integrity, interoperability, and accessibility of data across the IRB Assist ecosystem.
Key Responsibilities
Design the backend data architecture for the IRB Assist platform, including data models, schemas, and APIs.
Define how documents, feedback, user interactions, and metadata are stored, retrieved, and secured.
Ensure data privacy in all system designs.
Collaborate with Full Stack Developers and AI/NLP Engineers to integrate the data system with user interfaces and AI components.
Document architecture choices and support long-term scalability and maintainability.
Ideal Candidate
5+ years of experience in data architecture, data engineering, or backend systems design.
Strong background in designing secure, distributed systems with relational and non-relational databases.
Experience working with protected health information (PHI), HIPAA-compliant systems, or research data preferred.
Familiarity with AWS, Azure, or other cloud-based data infrastructure tools.
Comfortable working in a startup-like, early-stage environment with limited resources and high impact.