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About This Role
DESCRIPTION
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Have you ever thought about how Amazon drives growth in emerging countries? Do you have a passion for strategic, cross\-border e\-commerce? Do you enjoy working in an exciting, fast\-paced environment and solving complex problems with a global impact?
We are looking for a Product Marketing Manager (PMM) to own onsite marketing and merchandising strategy and execution for international customers shopping across borders on Amazon. You will build scalable mechanisms to manage retail merchandising operations, drive event and seasonal marketing programs tailored to cross\-border shoppers, and lead the building and adoption of AI automation for visual design and merchandising workflows in partnership with product and engineering teams.
You are expected to be an expert of knowing how to strategize running marketing onsite (mobile web/app/desktop/tablet) as well as well versed with Agentic AI workflows and tools, that makes you capable of not only running these channels but also build agentic workflows and automation in parallel and work with tech and product/UX teams to scale it globally. You will also be the central leader for onsite event's marketing and merchandising driving strategy and overseeing execution, reporting with our RBS partners (based out of India and China).
We are looking for a candidate who has hands on experience doing both of these as you will directly contribute in setting the strategic direction of the marketing function and programs to cast impact on customers from across 200\+ countries worldwide.
Key job responsibilities
1\. Scalable Mechanisms for Cross\-Border Retail Merchandising
- Build and own repeatable, scalable processes and playbooks for managing onsite merchandising across multiple international marketplaces and cross\-border shopping experiences
- Develop and maintain operational frameworks (calendars, briefs, tracking mechanisms) that enable consistent execution across regions and categories
- Create reporting dashboards and performance reviews to drive visibility into merchandising effectiveness and inform optimization decisions
- Partner with category, marketplace, and operations teams to align merchandising execution with cross\-border business goals
2\. Event \& Seasonal Marketing/Merchandising
- Own the onsite marketing and merchandising plan for tentpole events (Prime Day, Black Friday/Cyber Monday, Holiday, regional events) as they relate to cross\-border international shoppers
- Develop localized campaign strategies, creative briefs, and merchandising plans that resonate with international customer segments
- Coordinate with central events teams, category managers, and creative partners to deliver cohesive cross\-border event experiences
- Conduct post\-event analysis to measure performance, capture insights, and build institutional knowledge for future planning
3\. AI Automation for Visual Design \& Merchandising
- Identify and scope AI\-powered automation opportunities within visual design and merchandising workflows (e.g., automated asset localization, dynamic creative generation, layout optimization)
- Partner with product and tech teams to define requirements, test solutions, and drive adoption of AI tools across the merchandising workflow
- Develop business cases and success metrics for AI initiatives, tracking impact on speed, cost, quality, and customer engagement
- Stay current on generative AI and automation trends, advocating for innovative approaches within the team
4\. Reporting, Analytics \& Insights
- Own the end\-to\-end reporting framework for onsite marketing and merchandising performance across cross\-border experiences, including weekly/monthly business reviews
- Build and maintain performance dashboards tracking key metrics: traffic, impressions, CTR, conversion, revenue contribution, and customer engagement across merchandised placements
- Develop and automate recurring reports (WBR/MBR/QBR) that provide visibility into campaign performance, merchandising effectiveness, and cross\-border customer behavior
- Conduct deep\-dive analyses on merchandising experiments, A/B tests, and placement performance to surface actionable insights and optimization opportunities
- Partner with BI and data engineering teams to improve data pipelines, close measurement gaps, and enhance reporting capabilities using AI workflows
About the team
The Amazon X\-Border International Shopping and Prime team provides customers anywhere in the world access to a friction\-less shopping experience aligned with Amazon’s brand promise to offer vast selection, great prices, and shopping convenience. We aim to delight cross\-border (XB) customers by delivering a localized and personalized shopping experience, and a reliable and accurate delivery experience. We are looking for a dynamic and organized self\-starter to head our marketing function.
Culture: All roles in this org are very high visibility, with the opportunity to sit, learn and contribute to VP/SVP level meetings on a monthly basis. We are a very close knit org, leading with empathy and kindness and not shying away from getting our hands dirty to ensure we deliver the best customer experience (even if its not in our job description!). We work with international partners and celebrate every milestone. If you are looking for a team that works hard, makes history and HAS FUN \- this is it!
BASIC QUALIFICATIONS
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- 4\+ years of professional non\-internship marketing experience
- Experience using data and metrics to drive improvements
- Experience with Excel or Tableau (data manipulation, macros, charts and pivot tables)
- Experience building, executing and scaling cross\-functional programs or marketing campaigns from concept to completion
- Experience managing and measuring marketing performance in various channels
PREFERRED QUALIFICATIONS
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- Experience managing external partners to develop marketing programs
- Experience presenting ideas to various levels of an organization to gain support for initiatives
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 \- 109,100\.00 \- 160,000\.00 USD annually
Salary Context
This $109K-$160K range is in the lower quartile 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 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 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 ($134K) sits 37% below the category median. Disclosed range: $109K to $160K.
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.
Amazon.com AI Hiring
Amazon.com has 122 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager, AI Software Engineer. Positions span Seattle, WA, US, Santa Clara, CA, US, New York, NY, US. Compensation range: $128K - $338K.
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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