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About This Role
DESCRIPTION
At Amazon Ads, we're re-imagining the advertising landscape through advanced generative AI technologies and AI agents, revolutionizing how millions of customers discover products and engage with brands online. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle — from ad creation and optimization to performance analysis and customer insights.
The Ads Agent Fabric team delivers the AI infrastructure platform that accelerates agentic application development from months to weeks. We build the agent-builder SDK with pre-integrated Ads features, developer tools that accelerate the develop, test, and model evaluation lifecycle, and a fully-managed stateful runtime with integrated access to Ads services, data, and foundational models. Our platform solves core production challenges: managing execution state across multi-step reasoning and multi-agent orchestration, enabling resumability through checkpointing and task queues for long-running workflows, and providing memory primitives that enable stateful experiences where agents remember and learn across sessions.
You will build Tier-1 services to handle millions of requests per day, while maintaining response latencies in the milliseconds and meeting strict SLA requirements. You will develop distributed solutions with service-oriented architecture to build high-performance platforms powering the next generation of AI-driven advertising experiences. Our systems and algorithms operate on massive datasets using distributed frameworks. In this role, you will design new and rebuild existing solutions for the fast-growing scale of agentic infrastructure — including stateful runtime services, and pre-built platform components that agent-builder teams rely on to ship production agents.
You will not merely go through the full software development cycle but more importantly drive appropriate technology choices for the business, lead the way for continuous innovation, and shape the future of AI-powered advertising at Amazon.
We are a highly motivated, collaborative, and fun-loving team building at the forefront of AI agent infrastructure. We are entrepreneurial and have a bias for action with a broad mandate to experiment and innovate. This is an opportunity to make a significant impact on the future of AI agents in the Amazon Advertising business.
A successful candidate will have the satisfaction of seeing their work power thousands of AI agents serving millions of advertisers globally, broaden their technical skills across distributed systems and ML infrastructure, and work in an environment that thrives on creativity, experimentation, and product innovation.
Key job responsibilities
SDE-2s are autonomous contributors to their team's software. You deliver working features spanning the full software lifecycle including working backwards from customers, software design, implementation, testing, deployment, operations, and documentation.
SDE-2s coach others on producing high-quality code, using processes such as code review as a teaching mechanism. They ensure that when their software fails, the root cause is identified and eliminated with a permanent fix. They ensure the software they produce can be maintained and extended by those not familiar with the code by writing clear documentation and making good design choices.
SDE-2s participate in an inclusive, constructive dialogue with other team members and members of closely related teams. They train new team members on how their software is constructed, how it operates, and how it fits into the bigger picture.
About the team
Agent Fabric is Amazon Ads' AI infrastructure platform. We extend AWS AgentCore with advertising-specific capabilities and a managed, stateful runtime, cutting agent development from months to weeks. We own the platform end-to-end: the agent-builder SDK, the runtime that executes agents in production, and the knowledge and governance layers that keep it safe and accountable at scale. Our systems are in the critical path for GenAI projects across Amazon Advertising.
Stateful Runtime & Orchestration: an orchestration framework that turns a single agent turn into durable, resumable work: async conversations that persist after a turn ends and notify on completion, scheduled and event-driven workflows, and multi-step DAG execution with fan-out, cancellation, and retry. Checkpointing, task queues, and short- and long-term memory let agents resume and learn across sessions.
Knowledge Base: the RAG Knowledge Base that grounds advertiser answers across Sponsored Products, Sponsored Brands, DSP, reporting, and optimization. We run the KB agent, an OpenSearch vector index, and a daily ingestion pipeline spanning published and partner-curated content, with eval gates that enforce retrieval quality before every rebuild.
Pre-Built Ads Components: delegated advertiser authorization, privacy-safe logging, policy guardrails, real-time data access, and context handoff between agents.
Metering & Throttling: metering every invocation across model calls, tool calls, memory operations, and session time, then enforcing per-user and per-advertiser limits on a sub-millisecond path to protect the platform from abuse.
Operational Excellence: parallelization for low latency, human-in-the-loop approvals, end-to-end tracing, security isolation, cost attribution, and versioned deploys with rollback.
BASIC QUALIFICATIONS
- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- 1+ years of software development engineer or related occupational experience
- 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
- 1+ years of Object Oriented Design experience
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
- Experience programming with at least one software programming language
PREFERRED QUALIFICATIONS
- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent
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 - 158,100.00 - 213,800.00 USD annually
Salary Context
This $158K-$213K range is below the median for AI Product Manager roles in our dataset (median: $194K across 123 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Amazon.com, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 947 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($185K) sits 16% below the category median. Disclosed range: $158K to $213K.
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 Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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