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DESCRIPTION
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Lead strategic partnerships and drive transformative growth at Amazon Ads. As an AI Tech BD Sr. Manager, you will join the leadership team to focus on leading strategic deals, partnerships, and go\-to\-market strategies for the Creative X (CrX) and Retail Ad Service (RAS) organizations.
We’re building a new category of creative intelligence at Amazon Ads. Our team combines generative AI capabilities with advertiser\-centric design to simplify complex creative workflows and deliver measurable impact across the full marketing funnel. We serve advertisers of all sizes—from emerging brands to global enterprises—helping them create, optimize, and scale creative content across Amazon's advertising surfaces.
Our vision is to make personalized, high\-performing creative a foundational layer of how advertising works, not an add\-on feature. We’re positioned at the intersection of Amazon’s richest commerce signals and the largest advertising surfaces on the internet.
Within Creative X and Retail Ad Service, you will serve as the primary advocate for our largest customers—agencies, advertisers, and global partners—ensuring they maximize the value of our generative AI solutions for creative production, personalization, and optimization, while also developing and executing high\-impact partnerships with third\-party retailers. You’ll collaborate with internal stakeholders across Sales, Partner Development, GTM, Creative Services, Product, and Product Marketing to drive usage, enhance the customer experience, and accelerate product development. Additionally, you’ll identify, negotiate, and manage strategic partnerships with AI technology providers to bring new capabilities to our customers. This role demands a strategic mindset, deep expertise in AI\-infused creative tools, and a passion for building long\-term client relationships.
Key job responsibilities
Directly manage Tech BD members across both Creative X and Retail Ad Service to execute strategic partnerships and achieve business goals for each organization.
Own the executive relationships with strategically important customer and partner companies in the sector; institute regular mechanisms to cultivate trust and drive alignment with Amazon’s priorities. Provide executive sponsorship when needed for other BD efforts and act as the executive escalation management resource for Amazon leaders.
Build, orchestrate, and execute a GTM plan for forging and developing client relationships, driving adoption of both CrX and RAS solutions, as well as enabling product development based on customer requirements.
Serve as the primary point of contact for customers, ensuring seamless onboarding, product/feature adoption, and ongoing success with CrX and RAS products.
End\-to\-end deal experience: structuring and negotiating complex partnership agreements such as advertising deals, complex digital media transactions, and strategic ad technology relationships.
Strong financial acumen with the ability to analyze income, balance sheet and cash flow statements. Can construct an operating P\&L, model deals, and articulate cost/benefit analyses to key stakeholders.
Originate, execute, and manage partnerships to help accelerate product development and add new capabilities.
Partner with Account Executives and Partner Development Managers to drive customer engagement and identify growth opportunities.
Solicit voice\-of\-customer and synthesize feedback in digestible formats for use by internal partners in Product and Product Marketing.
Manage cross\-functional stakeholder relationships, acting as the bridge between customers and internal constituents
A day in the life
Your day will blend strategic leadership with hands\-on partnership management, driving high\-impact deals and shaping the future of Amazon Ads. You’ll review customer feedback from recent campaigns, identify opportunities to enhance our generative AI solutions, and collaborate with internal stakeholders to align on a joint roadmap. You’ll negotiate multi\-year partnership agreements with AI technology providers, balancing technical capabilities with business impact, and lead cross\-functional sessions to refine go\-to\-market strategies for new Retail Ad Service features. Throughout the day, you’ll act as the bridge between customers and internal teams, ensuring seamless onboarding, product adoption, and ongoing success while driving innovation through strategic insights and data\-driven decisions.
About the team
We are the Creative X and Retail Ad Service (RAS) team at Amazon Ads—driving the adoption of generative AI solutions and externalizing Amazon’s advertising technology to third\-party retailers. Our mission is to transform how advertisers create, personalize, and optimize campaigns through AI, while building strategic partnerships that accelerate product development and market growth.
You’ll work alongside sales, product, partner development, and engineering teams, ensuring our largest customers—agencies, advertisers, and retailers—maximize the value of Amazon’s AI\-powered tools. We value innovation, collaboration, and a passion for solving complex problems in fast\-paced, high\-visibility environments.
BASIC QUALIFICATIONS
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- Bachelor's degree or equivalent
- 12\+ years of Go\-To\-Market, Business Development, Sales, or Consulting experience
- 10\+ years of building and leading large teams and working in matrixed operating structures experience
- Experience working and communicating with multiple stakeholders, C\-level executives and cross functional teams or equivalent
- Experience in strategic thinking about business, enterprise software products, and new technology platforms and architectures or equivalent
- Experience negotiating VP\-level contracts with channel partners and agencies
- Experience collaborating with cross\-functional teams including Marketing, Product Management, Customer Service, Operations, Legal, Finance, and Senior Leadership
- Experience managing and developing high performance teams
PREFERRED QUALIFICATIONS
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- 10\+ years of managing and developing high performance teams experience
- Master's degree or equivalent
- Experience in e\-commerce, digital advertising, or media
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 \- 213,000\.00 \- 288,200\.00 USD annually
USA, WA, SEATTLE \- 193,700\.00 \- 262,000\.00 USD annually
Salary Context
This $193K-$288K range is above the 75th percentile 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 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 $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 ($240K) sits 12% above the category median. Disclosed range: $193K to $288K.
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 New York pay a median of $220,000 across 1,650 tracked positions.
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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