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About This Role
DESCRIPTION
AWS is seeking a dedicated, resourceful, and customer-obsessed Executive Assistant to support 3 senior leaders within the Agentic AI & Emerging Technologies organization: 1 Sr. Principal PMT-ES, 1 Director of Software Development, and 1 Vice President/Distinguished Engineer. The Executive Assistant will collaborate with various business partners in associated departments, as well as other Executive Assistants across the multiple global stakeholder teams within the organization.
The successful candidate will proactively streamline their leaders' calendars, enabling efficient day-to-day operations and allowing each leader to scale effectively. This role requires operating in a fast-paced, high-complexity environment with the ability to pivot quickly and act with minimal guidance. The role requires managing highly complex and fluid calendaring needs across three principals — including Executive Briefing Centers (EBCs), customer engagements, industry speaking events, and large-scale Amazon events, and cross-Amazon internal coordination — with speed, precision, and strong judgment.
A key aspect of this role is supporting customer-facing activities. Two of the three supported leaders participate extensively in external customer meetings, conferences, keynotes, and speaking engagements. The Executive Assistant will coordinate logistics for these engagements, including scheduling with internal account teams and external stakeholders, managing speaker submissions and deadlines, arranging travel for customer visits, and ensuring leaders are fully prepared for high-visibility interactions.
As an Executive Assistant, you will be responsible for a wide range of critical activities including complex calendar management across three principals, tracking key deliverables, coordinating travel and expenses, and organizing team activities and events (such as all–hands team meetings and employee engagement activities). Other responsibilities include, but are not limited to, coordinating meeting agendas, space planning, and ad hoc project coordination. A high level of integrity and discretion in handling confidential information, and professionalism in dealing with senior professionals inside and outside the company is important. In this role, you'll have the opportunity to act as an extension of our leaders in partnership with a supportive and collaborative EA community.
Working in a highly ambiguous environment, you will demonstrate initiative, complete tasks and projects quickly, and proactively take action without having to know the total picture. You will learn organizational structures and objectives of the teams you support, as well as build a strong internal network. You will apply superior attention to detail, great organizational skills, and the ability to meet tight deadlines and juggle multiple critical requests while maintaining flexibility, a sense of humor, and most importantly, grace under pressure. Over time, you will build deep and trusting professional relationships with the leaders and their teams, which will blossom into strong partnerships that provide opportunity for special projects with increased responsibility and long-term career growth.
Key job responsibilities
Ability to stay organized and operate effectively under pressure while supporting multiple principals with competing priorities
Communication style and stakeholder management, including professional engagement with external customers and event organizers
Cross-functional collaboration
Speed, efficiency, and quality of execution
Mechanism-building and process improvement
Expertly manage complex executive calendars across multiple time zones for 3 senior leaders and occasional coverage for other leaders when EAs are out of office.
Coordinate and manage logistics for customer-facing engagements, including EBCs, external speaking events, conference participation, and customer site visits.
Track speaker submission deadlines, event timelines, and preparation materials to ensure leaders are fully prepared for external engagements.
Maintain strict confidentiality regarding all calendar information and executive activities.
Conceptualize and execute a diverse range of team events, from social gatherings to large-scale staff meeting agendas, global team meetings, and off-sites.
Design and implement onsite team-building events that boost morale and foster collaboration.
Collaborate across Agentic AI & Emerging Technologies teams located primarily in North America to align on project goals and milestones.
Drive continuous improvement in team processes and project efficiency.
Have fun at work and help others do the same.
BASIC QUALIFICATIONS
- 3+ years of senior level leadership support, or 1+ years of Amazon experience
- High school or equivalent diploma
- Experience with Microsoft Office products and applications
- Experience with executive level calendar management
PREFERRED QUALIFICATIONS
- Experience in a fast-paced, high-tech company
- Experience managing multiple calendars
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 - 80,200.00 - 97,400.00 USD annually
Salary Context
This $80K-$97K 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 Web Services, 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 $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 ($88K) sits 58% below the category median. Disclosed range: $80K to $97K.
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 Web Services AI Hiring
Amazon Web Services has 71 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Engineer, Data Scientist. Positions span New York, NY, US, Bellevue, WA, US, Denver, CO, US. Compensation range: $97K - $299K.
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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