You will serve as the subject matter expert for data governance and policy on an NIH project, translating complex requirements into implementable technical controls for synthetic genomic data. This involves defining governance metadata, assessing disclosure risks, and collaborating with technical teams to ensure compliance with privacy and security standards.
Design, build, and deploy autonomous AI agents to automate internal operational workflows such as staff allocation and subcontractor management. Develop self-improving agent architectures and evaluation frameworks to ensure reliable, high-leverage performance.
You will translate user stories from non-technical teams into technical requirements and rapidly build working prototypes using the Microsoft Power Platform. You will also collaborate with cross-functional teams to implement agentic workflows and ensure successful handoffs for production deployment.
The Biomedical SME will map dbGaP variables to standardized vocabularies and design expert-adjudicated reference datasets for AI challenge evaluation. They will also provide technical authority on scientific integrity, benchmark validation, and FHIR-based interoperability.
The role involves creating authentic biomedical question and answer datasets to benchmark AI systems. Annotators will independently synthesize evidence from scientific literature to ensure factual accuracy and scientific reliability.
The engineer will guide cross-functional teams in developing AI prototypes and MVP solutions for medical research and public health use cases. They will also provide knowledge sessions and assist with data engineering tasks to support federal agency projects.
Design and build synthetic data generation pipelines to replicate high-fidelity, statistically representative datasets for AI testing. Collaborate with cross-functional teams to validate data fidelity and document privacy risk assessments for institutional stakeholders.
