Interested in this AI/ML Engineer role at Gates Foundation?
Apply Now →Skills & Technologies
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 Team
The Foundation Strategy Office is the primary function supporting the creation and evolution of our program level strategies, as well as exploring new paths to impact that align with our mission. This includes supporting the CEO, CFO and Chief Strategy Officer in key accountabilities including strategy development, strategy revision, strategy performance measurement, portfolio management, and special initiatives to accelerate the impact of the foundation.
Within the Foundation Strategy Office, the AI Steering Committee (AI SC) Secretariat serves as the strategic and operational backbone for setting Board Chair and Executive Leadership Team’s agenda on AI, best practice identification and execution support for high\-priority cross\-cutting AI strategy. The AI SC was established to help the foundation navigate the rapidly evolving AI landscape, enabling a cross\-organization group of foundation directors to serve as the AI “nerve center” for the organization through incorporation of external trends on AI into program strategies, elevation of best practices and cross\-organization prioritization of our funding and our voice on AI.
This role will be based out of the Seattle, Washington headquarters.Your Role
This role sits at the center of FSO's AI transformation work, operating across three interconnected workstreams: the AI Steering Committee secretariat, the Foundation's enterprise AI platform, and the FSO AI Learning Agenda.
The core of the role is pattern recognition and systems building across contexts. You will engage directly with staff across programmatic, operational, strategy, and IT functions to understand how knowledge work actually happens — and from that, identify where AI creates the most transformative value, at what scale, and what needs to be built. You will drive day\-to\-day progress across all three workstreams: developing use cases and requirements, building roadmaps, tracking the Foundation's AI portfolio, and designing learning experiences that shift how FSO staff work.
This position is a limited\-term position for 24 months.
What You'll Do
- Prototype and iterate AI\-enabled tools — copilots, agents, and workflow automations — moving from concept to usable internal solutions, with feedback loops and continuous improvement built in from the start.
- Identify high\-leverage use cases, map workflows and stakeholder needs with precision, and synthesize structured requirements that drive platform design and development prioritization.
- Define reusable patterns for AI components that scale across teams — including prompt design, retrieval approaches, and evaluation frameworks — partnering with IT to ensure solutions are production\-ready and architecturally sound.
- Scope and structure complex work products for distributed execution: determine optimal sourcing approaches (external vendors, AI Fellows, or other internal resources), decompose larger initiatives into discrete components assignable across sub\-groups, and ensure outputs can be integrated back into coherent, high\-quality deliverables.
- Monitor the external AI landscape and translate emerging developments into strategic implications for senior leadership.
- Conduct discovery with FSO staff to understand their goals and workflows, then design and facilitate learning experiences — workshops, hackathons, and coaching — that build AI fluency and shift how the division works.
- Act as a player\-coach: guiding staff to build their own solutions where appropriate, while directly developing higher\-complexity tools that require deeper systems thinking.
Deliverables
- Use Case and Requirements Library — Structured workflow analyses, stakeholder needs, and AI use cases organized to drive development prioritization and platform design.
- Platform Roadmap Inputs — Multi\-dimensional analyses mapping business relevance, data availability, feasibility, and strategic fit for development planning.
- FSO AI Learning\& AI Steer Co Operations AIRoadmaps — A living roadmap of capabilities and learning experiences, including net\-new components surfaced through direct staff engagement.
Your Experience
- Hands\-on experience building AI\-enabled workflows, automations, or tools — using LLM APIs (OpenAI, Anthropic, Gemini), no\-code/low\-code platforms (Zapier, Make, n8n), agent frameworks, or enterprise AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex) — with the technical intuition to evaluate feasibility, make informed build\-vs\-buy recommendations, and engage credibly with engineering teams on requirements and tradeoffs.
- Familiarity with data infrastructure concepts sufficient to identify upstream requirements for AI use cases — including data pipelines, APIs, structured vs. unstructured data, and retrieval\-augmented generation (RAG) architectures.
- Deep experience in strategy consulting, business analysis, product management, or a related field requiring structured problem\-solving and synthesis across complex stakeholder environments.
- Proven ability to pattern match and synthesize cross\-cutting themes across diverse domains, building structured frameworks, taxonomies, and roadmaps from what you find.
- Proven expertise mapping workflows and translating that understanding into development\-ready requirements and specifications.
- Significant experience preparing materials for and engaging with senior executives and board\-level stakeholders, including the ability to distill complex or ambiguous topics into decision\-ready formats.
- Significant experience communicating with a broad and diverse audience, including across potential barriers such as language and distance.
- Mastery level knowledge of end\-to\-end complex project management, including process design, team structure, critical thinking requirements, development of novel communication resources, and external/senior partner engagement.
- Mastery level knowledge of data analysis and visualization, including the ability to use data to create persuasive narratives and novel insights.
Preferred
- Experience in human\-centered design or service design — a practitioner's instinct for understanding what people need before designing solutions.
- Familiarity with knowledge management, organizational learning, or change management frameworks.
- Experience in global health, international development, or a related mission\-driven sector.
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 $190,100 to $294,700 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 $209,100 to $324,100 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 $190K-$294K range is above the 75th percentile 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 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($242K) sits 34% above the category median. Disclosed range: $190K to $294K.
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
AI roles in Seattle pay a median of $227,400 across 1,084 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 3,823 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.