Senior AI Engineer (3-Year LTE)

Bill & Melinda Gates Foundation•Seattle, WA

36d•$203,100 - $314,900•Onsite

About The Position

The Institute for Disease Modeling (IDM) is seeking a senior generalist engineer to design and build modern AI systems, including agents, retrieval pipelines, evaluation harnesses, and their supporting backend services. This role is crucial for translating AI concepts into functional systems that have a real-world impact on global public good. The engineer will work within a collaborative, mission-driven team alongside IDM researchers and their collaborators, contributing to the development of AI solutions for global health and development policy. This is a 36-month limited-term position based in Seattle, WA, with relocation assistance provided.

Requirements

Bachelor’s degree in a technical field with 5+ years building production software, or equivalent experience.
Strong general-purpose backend engineering skills, including the ability to understand unfamiliar code, debug across systems, and ship reliable services.
Proficiency in Python, including for AI work (e.g., PyTorch, Hugging Face, or similar).
Hands-on experience building LLM-powered applications: retrieval-augmented generation (RAG), agentic workflows, tool use, and prompt engineering at production scale.
Experience designing and operating backend APIs and services in cloud environments (ideally Azure, but AWS or GCP equivalents are acceptable).
Experience building AI evaluations and observability, including measuring quality, cost, and latency of LLM systems and acting on the results.
Hands-on experience with data pipelines and ETL, MLOps/AI Ops workflows, and cloud data services.
Experience with Git, CI/CD, containerization (Docker), infrastructure-as-code, and broader DevOps practices.
Comfort making and defending architectural tradeoffs (e.g., managed service vs. self-host, fine-tune vs. prompt, agent vs. workflow) and mentoring others through them.
Comfort working directly with researchers and non-engineers, translating fuzzy problems into concrete software, and providing constructive feedback on proposed solutions.
Track record of taking projects from prototype to a state where others rely on them.

Nice To Haves

Advanced degree is a plus, not required.
Experience with vector databases, knowledge graphs, or information architecture for AI applications.
Experience with fine-tuning, distillation, or other model adaptation techniques.
Exposure to scientific, public health, geospatial, or climate-related datasets.
Engagement with the open-source AI community.
Ability to stand up lightweight interactive demos (Streamlit, Gradio) when needed to show work to non-engineering stakeholders.
Publications, patents, or other public artifacts that show depth in an area.

Responsibilities

Design and build agentic AI systems, including multi-step workflows, tool use, and autonomous task orchestration using modern LLM frameworks, and deploy them as reliable backend services.
Build and operate retrieval systems, encompassing ingestion, chunking, embeddings, vector search, and knowledge-graph-backed retrieval.
Design AI evaluation pipelines and benchmarks to assess the improvement of agents, models, or retrieval systems, and monitor them in production.
Architect, implement, and maintain scalable backend services and APIs for use by other engineers, researchers, and applications.
Make senior-level architectural decisions regarding model choice, hosting (Azure OpenAI vs. self-hosted), framework selection, and infrastructure tradeoffs, and mentor other engineers on AI application patterns.
Develop data pipelines and workflows utilizing Azure (Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Databricks) and Hugging Face.
Harden successful prototypes into production-ready systems, including implementing tests, observability, cost and latency monitoring, failure handling, and documentation.
Collaborate directly with researchers, analysts, and program staff to translate complex domain problems into shippable systems for global health and global development.
Identify knowledge, data, or tooling gaps in the operational settings and propose pragmatic solutions.