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 Strategic Communications team leads global reputation and issue shaping that advances the foundation’s three goals and protects and strengthens the foundation’s brand through media intelligence; proactive/reactive media engagement and coordination; and always on monitoring and rapid response.Your Role
The Gates Foundation is at an inflection point in its AI engagement. With a growing portfolio of frontier lab partnerships, active deployments across health, education, and agriculture, and foundation leaders increasingly visible as a public voice on AI for good, the foundation needs a dedicated communications strategist to support enterprise communications on the topic.
This role will work with the Deputy Director, Strategic Communications, and work across program teams, frontier lab partner comms leads, regional offices, and the broader comms division colleagues. The role is equal parts strategist and implementer — someone who can hold the big picture while driving execution across a complex, fast\-moving environment.
- *This position is a limited\-term position for 24 months. Relocation will be provided.*
Application Deadline: July 15, at 8:59pm PDT
What You’ll Do
- Develop a clear, differentiated foundation narrative on AI for Good. Develop positioning statements, FAQs, narrative frameworks that can be adapted across audiences and formats.
- Conduct a full audit of upcoming moments — partnership announcements, product/relationship milestones, speaking opportunities, global convenings — and develop a peaks\-and\-valleys editorial calendar that creates sustained narrative presence
- Define a POV for broader public audiences — not just policy and philanthropy insiders — on why AI for global equity matters and what's at stake
- Develop and lead the execution of a proactive media strategy for the foundation's work on AI. Includes the development of relevant media resources, journalist relationship development, outlet prioritization, and collaboration with other divisions and regions on proactive storytelling around AI.
- Identify and develop opportunities for foundation voices to lead on AI — op\-eds, essays, panel appearances, keynotes, podcasts.
- Develop a pipeline of stories that connect our AI partnerships to real\-world impact.
- Lead integration of AI communications into the foundation's priority campaigns.
- Convene colleagues in the communications division to discuss the strategy and how to implement it across functions.
- Work with regional communications teams to share the strategy framework and support adaptation for local geographies, audiences, and political contexts
- Participate in enterprise AI conversations and interact with senior AI leaders at the foundation representing the communications team.
Your Experience
- Bachelor’s degree required with 10\+ years of experience or equivalent.
- Experience working with frontier AI and/or emerging technology organizations on communications.
- Global health or international development experience in a foundation, multilateral, or government organization.
- Strong initiative, organizational skills, and ability to work autonomously and cross\-functionally while remaining flexible to changing tasks, priorities, and roles and responsibilities.
- Proven ability to handle complex, multi\-stakeholder initiatives with distributed leadership.
\*\**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 $173,000 to $259,600 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 $190,300 to $285,500 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 $173K-$259K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $173K to $259K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Gates Foundation AI Hiring
Gates Foundation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Seattle, WA, US. Compensation range: $259K - $259K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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