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DESCRIPTION
Executive Assistant – Amazon Ads AI Infrastructure
Basic Qualifications
5+ years of senior level leadership support, or 3+ years of Amazon experience
High school or equivalent diploma
Experience with Microsoft Office products and applications, including Word, PowerPoint, Excel, Outlook, SharePoint, etc.
Experience with executive level calendar management
Experience with domestic or international travel coordination, or experience managing multiple calendars
Experience in a fast-paced, high-tech company
Preferred Qualifications
Experience leading process improvements
Experience in operational excellence using Six Sigma methodologies
Bachelor's degree or equivalent
Experience building scalable programs and repeatable processes, leveraging various tools and methods to create scale and efficiency
Experience prioritizing and handling multiple assignments at any given time while maintaining commitment to deadlines
Strong communication, interpersonal, and analytical skills with attention to detail
Experience with or interest in AI/ML technologies and infrastructure
Description
Amazon Ads AI Infrastructure is seeking an exceptional Executive Assistant to support the Vice President in a fast-paced, highly technical environment. is the foundational AI infrastructure organization within Amazon Ads, powering the machine learning and AI systems that drive advertising products and experiences for millions of advertisers and customers worldwide. Our team spans engineering, applied science, data engineering, and product, and we partner closely with teams across Amazon Ads to build and scale the AI/ML platforms that fuel innovation.
This is an internal-facing role — our partners and stakeholders are other teams within Amazon Ads and across Amazon. The EA will be a critical partner to the VP, enabling them to focus on the highest-priority work, and will serve as the lead EA for the broader ACI organization. The right person will develop a strong understanding of the organization, its leaders, priorities, and operating rhythms, and use that knowledge to improve decision velocity, communication flow, and execution across the leadership team.
The strongest EAs do more than execute tasks. They develop enough organizational understanding to exercise judgment on behalf of the executive they support — knowing the VP's priorities, leadership team, and working style well enough to make sound decisions independently rather than requiring direction for every one.
Key Job Responsibilities
Executive Calendar and Priority Management
Own the VP's executive calendar with strategic prioritization across multiple time zones, balancing the purpose and urgency of competing requests rather than simply filling available time.
Protect time for important, non-urgent work and build a calendar that is realistic — including transition time, preparation, meals, and travel.
Create coordination mechanisms across the VP's leadership team, Amazon Ads senior leadership, and cross-functional partner organizations.
Develop enough organizational context to recommend appropriate delegates and minimize unnecessary rescheduling once commitments are made.
Anticipate scheduling conflicts and resolve them proactively, maintaining alignment with VP priorities.
Organizational Coordination and Communication
Serve as the connective tissue of the organization, ensuring information, decisions, and follow-up move efficiently across the VP, leadership team, and key internal stakeholders.
Proactively surface important developments, anticipate coordination needs before they become blockers, and create clarity when ownership, timing, or next steps are ambiguous.
Use the right communication channels with appropriate urgency, distinguishing what requires escalation from what can be resolved independently.
Build strong working relationships with the VP's leadership team, peer EAs across Amazon Ads, and key partners across the company.
Track important commitments and ensure appropriate follow-through.
Meetings, Events, and Team Experiences
Plan and execute a range of meetings and events, including leadership offsites, tech strategy reviews, planning sessions, team-building activities, and org-wide events.
Handle all event logistics — venue, catering, room setup, transportation, and materials preparation — ensuring participants have what they need in advance.
Coordinate schedules across multiple leaders and partner organizations, anticipating logistical issues before they arise.
Partner with leaders and other EAs to create well-run, thoughtful experiences.
Support space planning for a globally distributed engineering and science organization spanning multiple buildings and locations, including seating allocation, office moves, and reconfigurations as the organization evolves.
Travel, Expenses, and Executive Logistics
Coordinate complex domestic and international travel, including multi-city itineraries, visa requirements, time zone optimization, and contingency planning, with practical attention to preparation and transition time.
Manage changes and disruptions as they occur, and complete expense reports accurately and promptly.
Strategic Deliverables and Project Management
Track and drive completion of key deliverables across the VP's portfolio, following up on action items from leadership meetings to ensure timely closure.
Support ad hoc strategic projects with increased scope and visibility.
Design and implement process improvements to maximize efficiency and scalability across the EA community and broader organization.
A Day in the Life
A typical day as the Lead Executive Assistant to the VP of ACI combines organizational excellence with hands-on problem-solving. You'll orchestrate a complex executive calendar across global time zones, keeping it aligned with the VP's highest priorities while protecting space for the thinking and preparation that high-stakes technical leadership requires. You'll coordinate high-visibility meetings with senior leaders across Amazon Ads and partner engineering organizations, track critical deliverables, and ensure follow-through on commitments across a fast-moving leadership team.
You'll manage communications flow to and from the VP, surface important developments before they become problems, and help create clarity when ownership or next steps are ambiguous. Each day brings opportunities to solve complex logistical challenges, advance important organizational initiatives, and identify better ways for the leadership team to operate — not just managing the work around the organization, but actively helping the organization work better.
About the Team
Amazon Ads AI Infrastructure is the engine behind the AI and machine learning systems that power Amazon Advertising. We are a deeply technical, globally distributed organization of engineers, applied scientists, data engineers, and product leaders who build and operate the foundational platforms that enable advertising innovation at Amazon scale.
Our work is internal-facing — we partner with teams across Amazon Ads to deliver the infrastructure, tools, and ML platforms they need to build world-class advertising products. We move fast, think big, and take pride in building systems that operate reliably at massive scale.
We are a team of builders who are smart, collaborative, and genuinely enjoy working together. We take our mission seriously and our camaraderie seriously too. We are looking for a Lead EA who brings the same energy: sharp instincts, hustle, warmth, and a sense of humor. If you thrive in a fast-moving technical environment, love solving complex problems, and want to be at the center of an organization doing meaningful work at the intersection of AI and advertising, this team is for you.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - $82,700.00 - $129,800.00 USD annually
BASIC QUALIFICATIONS
- 5+ years of senior level leadership support, or 3+ years of Amazon experience
- High school or equivalent diploma
- Experience with Microsoft Office products and applications
PREFERRED QUALIFICATIONS
- Experience leading process improvements
- Experience in a fast-paced, high-tech company
- Experience designing processes to maximize efficiency
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, SEATTLE - 82,700.00 - 129,800.00 USD annually
Salary Context
This $82K-$129K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Amazon.com, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($106K) sits 50% below the category median. Disclosed range: $82K to $129K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Amazon.com AI Hiring
Amazon.com has 65 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist, AI Product Manager. Positions span Seattle, WA, US, New York, NY, US, Bellevue, WA, US. Compensation range: $129K - $325K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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