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DESCRIPTION
Amazon Advertising is dedicated to driving measurable outcomes for advertisers, agencies, and entrepreneurs. Our ad solutions leverage Amazon's innovations and insights to find, attract, and engage intended audiences throughout their daily journeys. If you're interested in joining a rapidly growing team working to build a unique, world-class advertising group with a relentless focus on the customer, you've come to the right place.
The Sales GTM (SGTM) organization is the voice of the customer and the field back to Ads Product, as well as the business owner for driving customer outcomes through adoption of product solutions. This team owns the relationship with each Product org, produces a prioritized set of Sales priorities, and influences requirements, roadmap decisions, and trade-offs. They develop and implement GTM strategies and sales plays for both existing capabilities and new launches to drive adoption and advertiser maturity.
Agentic commerce is rewriting how consumers discover and buy, and Amazon sits at the center of that shift. Alexa for Shopping (the unified experience that brings together Rufus and Alexa+) now lets customers ask questions in the main Amazon search bar, build personalized shopping guides, compare products dynamically, and automate cart-building and routine purchases. Advertisers can be part of that conversation through sponsored ads, Sponsored Products and Sponsored Brands prompts, Alexa+ Conversational Ads, and Prime Video Sponsored Tiles, while AI-powered tools like Creative Agent and Amazon Ads Agent turn weeks of campaign work into minutes. The Large Customer Sales (LCS) sales team, AEs and AMs alike, needs to walk into every advertiser conversation with a clear, compelling, and current story about what this means for their business.
The GTM Specialist for Agentic Commerce & AI-Powered Advertising will be responsible for building and maintaining the content, playbooks, and enablement materials that bring the agentic commerce narrative to life for the LCS sales team. This role translates what's happening across conversational shopping, AI-powered campaign tools, and tentpole event strategy into actionable GTM execution that sellers can use with confidence. The right candidate is a first adopter of AI tools, proactively shares what works with the broader team, and is comfortable forming a strong point of view and pushing back when the field's needs aren't being met.
Key job responsibilities
- Developing and maintaining agentic commerce sales content across multiple formats including pitch decks, one-pagers, playbooks, FAQ documents, and insights packages, ensuring the LCS sales team has current, accurate materials that translate AI and conversational shopping complexity into clear advertiser narratives.
- Executing GTM strategies for agentic and conversational advertising products across LCS, including new betas and launches across Alexa for Shopping (sponsored ads, Sponsored Products and Sponsored Brands prompts), Alexa+ Conversational Ads, Prime Video Sponsored Tiles, and AI-powered tools such as Creative Agent and Amazon Ads Agent, with clear adoption metrics and feedback loops.
- Building and maintaining tentpole event playbooks (Prime Day, Holiday, Back to School, Black Friday/Cyber Monday) that incorporate conversational search insights and agentic commerce behavior signals into both pre-event pitch positioning and post-event performance analysis.
- Surfacing the voice of the LCS field back to Agentic Commerce, Stores, and Sponsored Ads product teams by consolidating advertiser feedback, adoption blockers, and segment-specific needs into structured inputs that inform roadmap priorities.
- Partnering with Product Marketing on agentic commerce narratives and positioning that connect capabilities like Alexa for Shopping, conversational ad formats, and Brand 360 to the advertiser value story: intent-rich conversations, built-in commerce completion, and closed-loop measurement from impression to conversion, ensuring LCS-specific messaging is distinct and actionable.
- Tracking product adoption and revenue trends across agentic commerce-adjacent products to surface insights for the LCS team, identifying high-impact opportunities and at-risk accounts that require targeted enablement.
- Supporting cross-functional alignment with Stores, Alexa+, and related product teams by preparing materials, tracking action items, and ensuring advertising outcomes are represented in joint reviews and planning forums.
- Leveraging AI tools and automation to accelerate content creation, streamline reporting, and scale GTM delivery, while championing new tool adoption and sharing best practices across the GTM team.
- Operating beyond the immediate scope of agentic commerce when needed, building context on adjacent products and cross-functional initiatives to ensure GTM strategies connect to the broader advertiser experience.
BASIC QUALIFICATIONS
- 5+ years of developing, negotiating and executing business agreements experience
- 5+ years of professional or military experience
- 5+ years of Go-To-Market, Business Development, Sales, or Consulting experience
- 5+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
- 5+ years of in program management, workforce strategy development, or metrics-driven decision making experience
- Bachelor's degree
- Knowledge of publisher ad tech stacks and/or advertising technology
- Experience developing strategies that influence leadership decisions at the organizational level
- Experience managing programs across cross functional teams, building processes and coordinating release schedules
- Experience leading complex, multi-year initiatives that may be cross-functional and/or span business and technology
PREFERRED QUALIFICATIONS
- Experience interpreting data and making business recommendations
- Experience designing, implementing, and scaling workforce development programs across large-scale operations, including forecasting workforce needs and leveraging analytics to drive program decisions
- Experience interpreting data and making business recommendations across leadership and cross-functional teams
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, NY, New York - 162,700.00 - 220,200.00 USD annually
Salary Context
This $162K-$220K range is above the median 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. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($191K) sits 10% below the category median. Disclosed range: $162K to $220K.
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 New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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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