Interested in this AI/ML Engineer role at Gates Foundation?
Apply Now →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 Communications Division, led by the Chief Communications Officer, oversees the foundation’s global communications strategy. The division brings together internal and external communications to advance the foundation’s mission to create a more equitable world.
The Insights \& Analytics Team helps colleagues within the Gates Foundation and our key external partners understand public discourse and sentiment on the issues that we work on. We use a variety of research methods and tools to understand what audiences care about, how they consume information, and what they think about issues from malaria eradication, to Artificial Intelligence, to charitable giving. The team includes experts in digital media analysis, information disorder, campaign evaluation, and public opinion; with team members based in the US, UK and Kenya.Your Role
The Program Officer, Knowledge Management andAI Enablement is responsible for stewarding diverse data assets and translating complex information into structures and systems that can be leveraged by developing AI systems across the Communications Division. Reporting into the Deputy Director, Insights \& Analytics, this role combines technical architecture, applied AI expertise, and change leadership to ensure the AI tools built and used by colleagues are built on robust data architecture.
You will:
- Serve as a data and knowledge steward, ensuring key data assets are organized, governed, and accessible for analysis and learning.
- Act as a bridge between data, analysis, and communications, translating complex findings into clear and usable formats.
- Demonstrate frontier AI fluency, particularly in how data must be structured, organized and maintained, so as to make best use of developing AI knowledge systems.
- *This is a limited\-term position that will go until the end of December 2027\. Relocation will not be provided.*
What You'll Do
- Design and build data infrastructure to support the development and use of advanced AI tools, including data pipelines, governance, and documentation.
- Enable AI readiness across data, processes, and organizational culture to support sustained adoption.
- Work with diverse data types—including communications data such as media monitoring (social and traditional), public opinion research, and network analysis—across qualitative and quantitative formats.
- Oversee cataloging, coding, tagging, and organizing data to ensure it is usable for AI\-enabled analysis and reporting.
- Prepare data for analysis and reporting, supporting analytical investigations and synthesis across teams.
- Apply quantitative analytical tools and methods to support evaluation, learning, and insight generation.
- Use contemporary AI tools at a sophisticated user level to support data analysis, synthesis, and knowledge management workflows.
- Identify high\-value opportunities for AI to improve communications and programmatic work and surface barriers to adoption.
- Develop or oversee the development of bespoke AI tools and applications tailored to communications and influence\-related use cases.
- Evaluate emerging AI tools and platforms for applicability, scalability, and responsible use across the division.
- Partner closely with leadership to translate identified impediments to AI adoption into actionable decisions and investments.
- This role includes up to 20% domestic/international travel.
Your Experience
- Bachelor’s degree required; advanced degree in information science, data science, social science, public policy, or a related field preferred.
- 5\+ years of experience in knowledge management, data analysis, research, evaluation, or a related role.
- Demonstrated experience working with multiple data types, including qualitative and quantitative datasets.
- Experience organizing, tagging, and managing data for analysis, reporting, and long\-term reuse.
- Experience developing data visualizations using common visualization tools or platforms.
- Working knowledge of quantitative analytical software and methods.
- Fluency with contemporary AI tools for data analysis and sensemaking (sophisticated user level; not developer).
- Strong analytical, synthesis, and communication skills, with the ability to tailor outputs to varied audiences.
- *Must have unrestricted work authorization in the country where this position islocated.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 $143,000 to $214,400 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 $157,300 to $235,900 USD. As a mission\-driven organization, we strive to balance competitive pay with our mission. New hires 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 $143K-$214K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $143K to $214K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Gates Foundation AI Hiring
Gates Foundation has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, Seattle, WA, US. Compensation range: $214K - $294K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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