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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 Team
Within Business Operations and the IT organization, the Enterprise Data Solutions (EDS) team designs and delivers enterprise\-scale platforms and services across Knowledge Management, Artificial Intelligence (AI), Data Engineering, Business Intelligence, Data Infrastructure, and Health Data Platforms.
The EDS team’s mission is to empower the Foundation with reliable, innovative, and data\-driven solutions that enable timely, informed decision\-making and support critical operational workflows.
Central to this mission is the development of an AI\-powered intelligent data and AI platform that enables employees to discover and interact with connected knowledge through conversational experiences. The Knowledge Mgmt \& Insights (KMI) is an AI\-first team and leverages technologies such as large language models (LLMs), knowledge graphs, and modern data platforms to deliver scalable, impactful enterprise\-wide AI solutions.
- This position is a limited\-term position for 24 months and located in our Seattle, Washington office. This is a hybrid role with a requirement of 50% in office.
Your Role
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As a Senior AI Engineer, you are a highly skilled individual contributor responsible for designing, building, and deploying AI solutions that support foundation and affiliate staff around the world. You bring strong technical expertise and a hands\-on approach to developing scalable, secure, and reliable AI systems that address real organizational needs.
You translate defined problem statements into effective technical solutions, partnering closely with the team and contributing directly to implementation and delivery. In this role, you play a key part in executing AI initiatives—designing solutions, integrating systems, and ensuring solutions are production\-ready and aligned with engineering best practices.
You collaborate with Lead Engineers and cross\-functional partners to deliver high\-impact AI capabilities, while proactively identifying opportunities to improve performance, usability, and scalability. You contribute to code quality, documentation, and shared standards, helping strengthen the team’s engineering discipline.
You also support other engineers through knowledge sharing and collaboration, while continuing to grow your own expertise in AI technologies and approaches. Your work enables innovative access to knowledge assets at the foundation, helping drive better decision\-making and streamline workflows to advance the organization’s mission globally.
What You’ll Do
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*Your work will include:*
- Develop and implement agentic AI systems, including multi\-step workflows, tool use, and autonomous task orchestration using LLM\-based frameworks
- Design, build, and deploy scalable AI/ML solutions using Python and cloud\-native services in Microsoft Azure (e.g., Azure OpenAI, Azure Foundry, Azure AI Search)
- Integrate AI capabilities into enterprise applications and workflows through APIs, microservices, and Azure\-based architecture
- Translate business and technical requirements into robust, production\-ready solutions, balancing performance, scalability, and maintainability
- Collaborate with engineers, product managers, and stakeholders to deliver AI solutions aligned to requirements
- Contribute to solution design and architecture discussions, including tooling and implementation trade\-offs
- Use AI\-assisted development tools (e.g., Claude Code, Codex, GitHub Copilot) to improve development efficiency while maintaining strong code quality
- Contribute to code reviews, automated testing, and CI/CD pipelines to ensure reliable and secure deployments
- Deploy, monitor, and optimize AI solutions in Azure, addressing performance, cost and reliability
- Troubleshoot and resolve issues across development and production environments
- Contribute to reusable components, shared frameworks and engineering standards for AI development across the team
- Support data preparation, prompt engineering, and evaluation processes to improve model performance and outcomes
- Stay current with emerging AI technologies and proactively identify opportunities to apply them to enhance solutions where relevant
Your Experience
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Bachelor’s degree or equivalent experience; typically requires an advanced degree or equivalent experience and a minimum of 8 years of prior relevant experience.
- Strong analytical and problem\-solving abilities, with a commitment to operational excellence, continuous learning, and raising the bar on engineering practices
- Strong programming skills in Python
- Hands\-on experience with Claude Code, GitHub Copilot, Codex or other AI\-assisted coding tools
- Hands\-on experience with state\-of\-the\-art LLMs, agentic systems, and RAG architecture, graph technologies and practical knowledge of embeddings, tokenization, evaluation, observability, and frameworks such as LangChain and LlamaIndex.
- Experience deploying, monitoring, and maintaining ML/LLM systems in production.
- Deep understanding of data privacy, security, and compliance considerations when building AI\-powered systems.
- Excellent communication and influencing skills, with the ability to engage immediate and extended team, build consensus, and move work forward in ambiguous or rapidly evolving environments.
- 3\+ years of professional experience with cloud technologies preferably Azure
- Proven ability to prototype, iterate, and productionize innovative AI solutions that deliver measurable business value.
- Knowledge of ethics in AI and responsible AI development.
- Experience with agile development methodologies such as Scrum or Kanban
Additional Requirements
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- Ability to travel as needed.
- Ability to participate in on\-call duties
- Must be able to work effectively across geographic and cultural boundaries.
- Commitment to the foundation’s mission and values, including diversity, equity, and inclusion.
- Must be able to legally work in the country where this position is located without visa sponsorship. 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 $157,400 to $236,000 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 $173,100 to $259,700 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 $157K-$236K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($196K) sits 8% below the category median. Disclosed range: $157K to $236K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
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: $236K - $236K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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