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About This Role
The Foundation
We are the largest nonprofit fighting poverty, disease, and inequity around the world. Founded on a simple premise: people everywhere, regardless of identity or circumstances, should have the chance to live healthy, productive lives. We believe our employees should reflect the rich diversity of the global populations we aim to serve. We provide an exceptional benefits package to employees and their families which include comprehensive medical, dental, and vision coverage with no premiums, generous paid time off, paid family leave, foundation\-paid retirement contribution, regional holidays, and opportunities to engage in several employee communities. As a workplace, we’re committed to creating an environment for you to thrive both personally and professionally.
The Team
The Institute for Disease Modeling (IDM) is an embedded research institute with the Gates Foundation. We develop, use, and share computational modeling tools, and promote quantitative decision\-making to support global efforts to eradicate infectious diseases and achieve permanent improvements in the health of those most in need. The IDM team is composed of research scientists and software developers who develop and prototype computational tools to advise on global health and development policy and identify and address critical knowledge gaps. IDM is a multifaceted organization with a work environment defined by innovation and teamwork. As part of our work, we routinely collaborate with groups at the World Health Organization, UNAIDS, the Centers for Disease Control, PATH, ministries of health, as well as universities and research institutes across the globe.Your Role
We are looking for a senior generalist engineer who is energized by designing and building modern AI systems agents, retrieval pipelines, evaluation harnesses, and the backend services that hold them together. You are passionate about well\-architected software, you make pragmatic build\-vs\-buy and self\-host\-vs\-API calls, and you reach for the simplest approach that actually solves the problem. You will work on projects with real\-world impact for global public good, integrated into a collaborative, mission\-driven team alongside IDM researchers and their internal and external collaborators. You will be essential in turning AI ideas into working systems agents, services, evaluations, and the connective tissue that lets a small team ship a lot.
- *This is a 36\-month limited\-term position based in Seattle, WA. Relocation will be provided.*
What You’ll Do
- Design and build agentic AI systems — multi\-step workflows, tool use, and autonomous task orchestration using modern LLM frameworks — and ship them as reliable backend services.
- Build and operate retrieval systems: ingestion, chunking, embeddings, vector search, and knowledge\-graph\-backed retrieval where it earns its keep.
- Design AI evaluation pipelines and benchmarks so the team can tell whether an agent, model, or retrieval system is actually getting better — and monitor them in production.
- Architect, implement, and maintain scalable backend services and APIs that other engineers, researchers, and applications build on top of.
- Make senior\-level architectural calls — 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 leveraging Azure (Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Databricks) and Hugging Face.
- Harden successful prototypes into production: tests, observability, cost and latency monitoring, failure handling, and documentation that let other people trust and reuse them.
- Collaborate directly with researchers, analysts, and program staff to translate fuzzy domain problems into shippable systems for global health and global development.
- Identify knowledge, data, or tooling gaps in the settings in which we work and propose pragmatic solutions.
Your Experience
- Bachelor’s degree in a technical field with 5\+ years building production software, or equivalent experience. Advanced degree is a plus, not required.
- Strong general\-purpose backend engineering: you can pick up unfamiliar code, debug across systems, and ship services that hold up in use.
- 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 fine.
- Experience building AI evaluations and observability — 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 (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 — able to translate fuzzy problems into concrete software, and to push back when the simplest answer is “we don’t need to build that.”
- Track record of taking projects from prototype to something other people rely on.
Other Attributes
- 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 — but not required and not weighted over shipped work.
*\*\*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 (eg: H\-1B, O\-1, L\-1, E, OPT, STEM\-OPT, CPT, TN, J\-1, etc.).*
The salary range for this role is $186,400 to $288,800 USD. We recognize high\-wage market differences in Seattle and Washington D.C., where our offices are located. The range for this role in these locations is $203,100 to $314,900 USD. As a mission\-driven organization, we strive to balance competitive pay with our mission. New hire salaries are typically between the range minimum and the salary range midpoint. Actual placement in the range will depend on a candidate’s job\-related skills, experience, and expertise, as evaluated during the interview process.
Hiring Requirements
As part of our standard hiring process for new employees, employment will be contingent upon successful completion of a background check.
Candidate Accommodations
We’re committed to providing an inclusive and accessible hiring experience for all candidates. If you have a disability or medical condition and need an accommodation at any stage of the application or interview process—such as an ASL interpreter, alternative interview format, or physical accessibility support—we’re happy to help. Please contact [email protected] with the position number and a brief description of your accommodation needs. Requests will be handled confidentially.
Inclusion Statement
We are dedicated to the belief that all lives have equal value. We strive for a global and cultural workplace that supports ever greater diversity, equity, and inclusion — of voices, ideas, and approaches — and we support this diversity through all our employment practices.
All applicants and employees who are drawn to serve our mission will enjoy equality of opportunity and fair treatment without regard to race, color, age, religion, pregnancy, sex, sexual orientation, disability, gender identity, gender expression, national origin, genetic information, veteran status, marital status, and prior protected activity.
Salary Context
This $186K-$288K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Gates Foundation, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($237K) sits 33% above the category median. Disclosed range: $186K to $288K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Gates Foundation AI Hiring
Gates Foundation has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $288K - $294K.
Location Context
AI roles in Seattle pay a median of $228,000 across 1,009 tracked positions. That's 14% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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