Interested in this AI/ML Engineer role at Gates Foundation?
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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 Gender Equality Division’s mission is to ensure women and girls in Africa and South Asia can enjoy good health, make their own choices, earn their own money, and be leaders in their societies. When women and girls have an equal chance to thrive and lead, everyone benefits. We know that women and girls are not a monolith. A woman’s experience and the barriers she faces are different depending on factors like where she lives, how old she is, and how much money her family has, alongside other factors including her race, caste, and education level. We seek to address these compounding barriers through deep partnership with our grantees. As a philanthropy, our role is to listen to and learn from these experts. We take risks in new areas, prove concepts, and bring these ideas to governments, partner organizations, and private sector companies to scale. The Gender Equality Division builds off our existing strengths as a foundation in areas like global health and data, partnering closely with all our global divisions.
The Maternal, Newborn, Child Nutrition & Health (MNCNH) team works to eliminate preventable mortality and morbidity among women, newborns, and children in low-resource settings. We do this by funding and accelerating a portfolio of high-impact products targeted at the moments of greatest biological vulnerability across the life-course. Our portfolio addresses pregnancy risk, maternal undernutrition, prematurity, neonatal encephalopathy, child wasting, neurodevelopment, and maternal morbidity through drugs, foods, microbes, devices, risk algorithms, and increasingly through AI and connected device technologies that extend the reach of these interventions where health systems are weakest. The portfolio spans the full development continuum from early discovery through implementation research and country introduction. A multidisciplinary team of physician scientists, epidemiologists, engineers, nutritionists, and commercialization experts works alongside in-country partners to integrate local context into research, development, and scale.Your Role
The Senior Program Officer (SPO), Software & AI, will serve as a technical and strategic lead for software, digital health, and AI-enabled products across the MNCNH portfolio. The role will help translate high-priority health use cases into products and platforms that are technically sound, user-centered, interoperable, and deployable in low-resource settings. The portfolio may include clinical decision support, risk-stratification tools, AI-enabled applications, digital workflows, software that connects to medical devices, and other data- and software-driven approaches that improve individual-level care and health-system performance. A flagship priority for this role is accelerating risk-stratification software layered on top of the foundation's AI-enabled hardware investments — spanning antenatal ultrasound, intrapartum monitoring, and postpartum/neonatal care — turning imaging and physiologic signal data into clinically actionable outputs. The SPO will hold grantmaking responsibility for a defined portfolio of investments and work closely with MNCNH technical leads, country teams, and partners to move promising concepts from prototype through validation, productization, integration, and scale.
While primarily anchored in MNCNH, this role will also serve as a shared technical resource for selected software and AI use cases across the broader Women’s Health portfolio. Success will require strong product and software-development judgment, fluency in rapidly evolving AI technologies — equally at ease with classical/deterministic modeling and generative AI methods, and able to judge which approach a given clinical use case actually calls for, and the ability to connect technical choices to real-world implementation constraints. The SPO will work across internal teams and with grantees, technology companies, software developers, researchers, implementing partners, governments, and other stakeholders to ensure that solutions are designed for local workflows, data environments, connectivity constraints, and sustainable country ownership.
What You’ll Do
- Lead and manage a portfolio of grants, contracts, and other investments focused on software, digital health, and AI-enabled products, from sourcing and diligence through structuring, execution, monitoring, and reporting.
- Translate MNCNH and selected broader Women’s Health priorities into clear software and AI product roadmaps, identifying the highest-value use cases, portfolio gaps, technical dependencies, and build-versus-partner decisions.
- Provide technical leadership across the software product lifecycle, including product requirements, user-centered design, architecture, software development, testing, deployment, maintenance, and product iteration.
- Guide the development and productization of AI-enabled tools, including clinical decision support, risk stratification, workflow automation, multimodal and generative AI applications, and software that connects to medical devices or other digital platforms.
- Define how imaging (e.g., ultrasound) and physiologic signal data (e.g., intrapartum and neonatal monitoring) pools are structured, labeled, and converted into validated, productized software outputs, partnering with the foundation's data team on shared standards and infrastructure.
- Assess technical architecture and integration requirements, including APIs, interoperability, data flows, identity and access, cloud/edge/local deployment, offline and low-bandwidth operation, and integration with existing health information systems and digital public infrastructure.
- Partner with grantees, developers, researchers, country stakeholders, and implementation partners, working closely with other Program Officers across the hardware and clinical portfolio and the foundation's data team, to ensure products are designed around frontline workflows and can move from prototype or pilot to reliable, maintainable, scalable use, including at regional deployment and pilot sites.
- Apply strong judgment on responsible AI, data governance, privacy, security, clinical safety, model performance, and human oversight, and establish clear evidence and decision criteria for when products are ready to advance, scale, pause, or stop.
- Stay closely connected to the evolving software and AI ecosystem, including emerging models, platforms, vendors, and technical approaches, and translate relevant developments into practical implications for the portfolio.
- Contribute to budgeting, risk management, and sequencing of investments, with clear milestones, decision points, technical deliverables, and outcomes that support effective stewardship of foundation resources.
- Track and communicate portfolio progress and translate complex technical, product, implementation, and market evidence into clear recommendations for team and foundation leadership.
- Foster coordination across MNCNH, Women’s Health, country teams, and relevant foundation-wide digital and AI teams so that use cases build on shared capabilities rather than creating duplicative or disconnected solutions.
- Build and sustain an external ecosystem of software developers, AI companies, researchers, digital-health experts, implementers, governments, and other partners needed to advance affordable and scalable solutions for low- and middle-income countries.
- Represent the Foundation externally, including at technical and industry meetings, partner convenings, advisory groups, and engagements with governments, technology companies, and implementing organizations.
Your Experience
- Advanced degree in computer science, software engineering, data science, biomedical informatics, engineering, medicine, public health, or another relevant technical or clinical discipline.
- Minimum 10 years of relevant experience developing, launching, and scaling software, digital-health, data, or AI-enabled products; experience with health or other high-stakes applications is strongly preferred.
- Deep technical and product knowledge in several of the following areas: software architecture, APIs and interoperability, mobile or web applications, cloud and edge computing, data platforms, artificial intelligence/machine learning, generative AI, clinical decision support, risk stratification, or software as a medical device.
- Demonstrated fluency in both classical/deterministic modeling (e.g., clinical risk scores, statistical and rules-based methods) and generative AI approaches, with sound judgment about which fits a given clinical use case. Experience working with imaging (e.g., ultrasound) and/or physiologic signal data (e.g., intrapartum or neonatal monitoring), and experience in a regulated medical device (SaMD) context, is strongly preferred.
- Demonstrated ability to translate user and program needs into product requirements and technical roadmaps, and to work effectively with engineering teams from early prototype through deployment and iteration.
- Strong understanding of responsible AI, data governance, privacy, security, model evaluation, clinical safety, and the evidence needed to support adoption and scale of software-enabled health products.
- Experience designing or deploying products in constrained environments, including low-connectivity settings, fragmented data ecosystems, limited device access, or complex government and health-system workflows.
- Demonstrated ability to set strategy, make portfolio and investment decisions, and manage complex programs with clear milestones, budgets, risk mitigation, and outcomes.
- Strong external network and demonstrated ability to stay current in a rapidly evolving technology landscape and distinguish durable capabilities from short-lived trends or vendor-specific solutions.
- Experience working across traditional disciplinary boundaries and with senior leaders, software engineers, data scientists, clinicians, researchers, governments, technology companies, and implementation partners.
- Excellent written, verbal, and presentation skills, with the ability to communicate complex technical concepts clearly to diverse audiences and build alignment across a highly matrixed environment.
- Proven ability to build and manage multi-stakeholder partnerships across technology, academia, industry, governments, and implementing organizations.
- Global health experience is strongly preferred; experience developing and deploying software or AI-enabled products in low- and middle-income countries and/or women’s, maternal, newborn, and child health is ideal.
- Ability to travel domestically and internationally up to approximately 30% of the time.
The salary range for this role is $212,800 to $329,800 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 $234,000 to $362,800 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 $212K-$329K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($271K) sits 28% above the category median. Disclosed range: $212K to $329K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
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: $329K - $329K.
Location Context
AI roles in Seattle pay a median of $226,100 across 1,045 tracked positions. That's 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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