Senior Officer, Model Frameworks – Global Health

Gates Foundation•Seattle, WA

About The Position

The Institute for Disease Modeling (IDM) at the Gates Foundation is seeking a Senior Officer, Model Frameworks to build and improve the software and AI capabilities behind IDM’s disease modeling efforts. This role sits within the Model Frameworks team and reports to the Senior Manager, Model Frameworks. This work is critical to accelerating the evidence-to-policy lifecycle in low-resource settings by reducing the time researchers need to produce validated model results, and by holding supporting technology to scientific standards of validity and reproducibility.

Requirements

Typically, 5–8 years of professional software engineering experience, or equivalent demonstrated impact in a research software or computational modeling context.
Hands-on experience building with generative AI, including agent design, tool use, and context management, in production or research-grade environments, along with a working grasp of how AI system quality gets measured and improved.
Knowledge of and hands-on experience with computational modeling, such as agent-based modeling (ABM), compartmental models, or statistical/ML approaches.
Demonstrated ability to deliver well-tested, maintainable code within a CI/CD-enabled engineering workflow.
Ability to work independently on multi-week tasks, communicate tradeoffs clearly, and adapt priorities based on manager and team feedback.
Must have unrestricted work authorization in the country where this position is located. The Foundation does not provide immigration-related sponsorship for this role. This includes direct company sponsorship and any work authorization requiring a written submission or other immigration support from the company (e.g. H-1B, O-1, L-1, E, OPT, STEM-OPT, CPT, TN, J-1, etc.).

Nice To Haves

Experience collaborating closely with researchers, applied scientists, or domain experts in global health or public health settings.
Familiarity with open-source software development practices, including contribution workflows, documentation standards, and community engagement
Exposure to cloud-based data and compute platforms used for large-scale analysis or simulation workloads.
Hands-on experience with production AI system design, such as orchestration across long-running tasks, evaluation harnesses, and guardrails, at a level that could help guide practice for a team.
Background in epidemiology, computational biology, infectious disease modeling, or scientific computing.
Experience working in LMIC contexts or designing software for use in low-resource or low-bandwidth environments.

Responsibilities

Produce well-tested, maintainable code with strong use of CI/CD, code review, and modern quality practices.
Use AI development tools to accelerate code development and improve quality, and share proven, best practice back to the team.
Build, evaluate, and deploy AI-enabled modeling components with sound technical rigor, including data pipelines, context engineering, tool interfaces, evaluation harnesses, and guardrails.
Work across IDM's modeling paradigms (agent-based, compartmental, statistical) to develop robust and reproducible framework capabilities.
Independently plan and execute work across concurrent projects, coordinating with teammates to keep deliverables on track with minimal supervision.
Communicate proactively when timelines or priorities shift and escalate risks clearly with proposed solutions.
Structure open-ended modeling problems into tractable components, driving clarity for the team on where to start.
Apply mathematical and AI/ML techniques creatively to solve complex modeling challenges and unblock peers when needed.
Track emerging AI methods and modeling tools relevant to the team and bring insights into current projects.
Design and build AI capabilities that refine how IDM researchers work, including summarization and synthesis of scientific literature, natural-language access to research data, structured extraction from unstructured sources, and agentic workflows that help automate research tasks.
Hold a high bar for scientific credibility, leveraging traceable output and human review where results inform policy decisions.
Build strong working relationships with IDM/PST researchers and LMIC-based research partners to deliver solutions that accelerate research outcomes.
Communicate clearly with both technical and research audiences, adapting to partner contexts and gaining buy-in for engineering approaches.
Exemplify team culture, DEI norms, and collaborative engineering practice for peers and more junior engineers.
Serve as a reliable go-to within the team for technical direction, design reviews, and problem escalation.