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DESCRIPTION
Advertising is being remade. The brands that win the next decade will be the ones that figure out how to deliver creative that is personal, performant, and produced at a scale no human team can match alone. Generative AI is the unlock. But generation alone is not the advantage. The advantage belongs to the system that connects creation to delivery to learning, and gets better with every impression.
Amazon Ads is building that system. We are investing in the models, the infrastructure, and the product intelligence to make personalized, brand-aligned creative a default capability for every advertiser, not a luxury reserved for the largest budgets. The work is early, the stakes are high, and the opportunity to define how this category develops is real. We are building that system. We need a Principal AI Creative Director to define what great looks like inside it and to make that definition teachable, scalable, and machine-readable.
Key job responsibilities
As Principal AI Creative Director for Personalization Optimization and Design (POD), you are the human judgment layer at the center of Amazon's AI creative system. Generative AI has commoditized asset creation. The durable differentiator is now creative direction: the taste, the standards, the brand intelligence, and the systematic thinking that ensures our system produces creative that builds brands, not just optimizes clicks.
This is not a role about using AI tools, though you must be among the most fluent practitioners working today. It is about understanding what makes a great commercial, a great piece of content, a great brand moment, and then decomposing that understanding into primitives: the story arc, the color logic, the compositional tension, the emotional beat, the brand signal. Those primitives are what our systems learn from, replicate, and eventually govern at scale.
You will operate as a principal-level individual contributor with org-wide influence, partnering directly with design, engineering, science, and product leadership across every surface (POD) owns.
A day in the life
Creative Direction and Quality Standards:
Define and own the creative quality bar for AI-generated assets across POD's full portfolio, including images, video, copy, and multi-format compositions. Establish brand intelligence frameworks that ensure AI outputs are aesthetically coherent, emotionally resonant, and brand-safe at scale. Act as the senior creative editor for AI-generated content, distinguishing exceptional work from "good enough" outputs and articulating the difference in decomposable, teachable terms.
Decomposing Creative into Primitives.
Lead the Systemization of Taste: Break down what makes great creative into its constituent elements: narrative structure, color theory, compositional hierarchy, emotional arc, pacing, and brand signal. Translate these primitives into structured frameworks, including prompt libraries, creative briefs, quality rubrics, and annotation schemas, that inform model training, DCO reward signals, and creative tooling. Define the primitives that power aesthetic moderation: the signals and thresholds that allow our systems to evaluate, filter, and rank AI-generated content without requiring human review of every asset. Partner with applied science and engineering to operationalize these as machine-readable inputs.
Hands-On Production and Prototyping:
Produce high-quality creative content independently across modalities, including image, video, and copy, using the leading AI creation tools. Bring that same hands-on fluency to prototyping: build working creative prototypes that demonstrate new customer experiences, surface trade-offs, and create decision points for design, product, and engineering leadership. This role operates in a demo-driven culture where prototypes move decisions. You are expected to contribute directly to that culture, not just direct others within it. Maintain current, deep fluency across tools including text-to-image (Midjourney, Adobe Firefly, Stable Diffusion), text-to-video (Sora, Runway, Kling), and LLM-powered copy and agentic workflows.
Defining New Creative Experiences:
Partner with design, product, and engineering to define what new AI-powered creative experiences should look and feel like for advertisers.
Translate Creative Vision into CX direction: How should a brand interact with a generative creative system? What does a great AI-assisted creative workflow feel like? Advise on the experience design for advertiser-facing tools, including Creative Studio, Video Generator, and the creative agent, ensuring AI capabilities are surfaced in ways that are legible, trustworthy, and creatively empowering.
Aesthetic Moderation and Governance:
Build and own the aesthetic moderation framework for POD: the multi-dimensional quality scorecard that defines what "premium creative" means in measurable, auditable terms across visual quality, brand alignment, emotional resonance, and format appropriateness. Establish governance processes, including quality thresholds, brand safety standards, and editorial review workflows, that scale without requiring human review of every asset. Partner with advertiser-facing teams to translate brand requirements into creative direction that AI systems can operationalize and enforce.
Cross-Functional Creative Leadership:
Serve as the creative authority in cross-functional conversations with engineering, science, product, and design leadership, earning influence through demonstrated quality and creative vision. Partner with the Design Technology team to ensure prototypes reflect a high creative bar before graduating into engineering roadmaps. Collaborate with PAO (Creative Personalization and Optimization) to ensure personalized creative variants maintain quality and brand coherence at impression level.
BASIC QUALIFICATIONS
10+ years of creative direction experience, with a portfolio demonstrating exceptional craft across visual design, brand identity, advertising, or digital creative
2+ years of demonstrated, hands-on work in AI content creation, including major campaigns, brand work, or personal projects that showcase advanced fluency with generative AI tools across image, video, and copy
Ability to produce finished creative content independently across modalities using AI tools, not just direct others to do so
Deep understanding of what makes great creative, including narrative structure, color theory, compositional hierarchy, emotional arc, pacing, and brand signal, and the ability to articulate these elements in precise, teachable, decomposable terms
Experience working at the intersection of creative and technology, collaborating with engineering, product, or data science teams to build creative systems
Demonstrated ability to influence without authority in large, cross-functional organizations
PREFERRED QUALIFICATIONS
Experience in advertising, e-commerce, or performance marketing contexts, with an understanding of how creative connects to business metrics, conversion, and brand equity
Experience building creative quality frameworks, brand standards, or design systems that scale across large organizations or product portfolios
Track record of translating creative intuition into structured, systematic frameworks, including prompt libraries, quality rubrics, annotation schemas, or training data curation
Familiarity with aesthetic moderation, content quality scoring, or automated creative evaluation
Familiarity with Dynamic Creative Optimization (DCO), personalization systems, or data-driven creative
Experience contributing to or advising on AI model development from a creative direction perspective, including feedback on outputs, training data curation, or quality reward signal definition
Proficiency with design tools (Figma, Adobe Creative Suite) to refine, annotate, and communicate creative direction alongside AI-generated outputs
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 - 201,800.00 - 273,000.00 USD annually
Salary Context
This $201K-$273K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($237K) sits 12% above the category median. Disclosed range: $201K to $273K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Amazon.com AI Hiring
Amazon.com has 53 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager, AI Safety. Positions span Culver City, CA, US, Arlington, VA, US, New York, NY, US. Compensation range: $160K - $297K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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