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About This Role
DESCRIPTION
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We're building the future of cloud financial management, and the hardest unsolved problem in it is AI cost. Customers are moving from experimenting with generative AI to running it in production at scale. Spend is driven by tokens, model choice, inference patterns, and increasingly by agents that make their own consumption decisions. The AWS Billing organization processes millions of events per second to deliver the cost, usage, and optimization insights that power financial decisions at the world's largest enterprises. We are now extending that foundation to give customers the ability to understand, allocate, control, and optimize AI spend with the same rigor they apply to compute and storage today.
As Senior Product Manager, you will execute AWS's strategy for AI cost governance. This is the highest\-ambiguity, highest\-leverage product space in the organization: the mechanics of tokenomics, attribution, and control for generative AI workloads are still being defined across the industry, and the decisions made here set the direction that partner service teams, enterprise customers, and eventually all industry players respond to. You will own the product bets that define the category.
In this role, you will define what it means to govern AI spend on AWS: how cost is attributed at the token, model, and agent level; how customers allocate that cost to teams, applications, and business units; and how they set and enforce controls before spend, rather than explaining it afterward. You will write the strategy documents and PR/FAQs for the most consequential launches, run the customer conversations, and make the hard trade\-off calls on scope, sequencing, and where AWS should lead versus follow. You will pressure\-test metering, API, and interface designs directly with engineering, and dig into consumption data and customer telemetry to understand where the real cost drivers and governance gaps are. You will own the measurement of whether these capabilities actually change customer behavior, including adoption, spend under active governance, and the degree to which cost visibility unblocks AI expansion, and build the review mechanisms that hold the organization accountable to it.
You will make the calls on how AI cost governance extends to emerging surfaces, including foundation model inference, agentic workloads, and third\-party and marketplace model consumption, and on when AWS should build new primitives versus extend existing cost management constructs. You will partner with engineering leaders, applied science leaders, and product leaders across Bedrock, AgentCore, and the broader AWS generative AI portfolio to ensure cost governance is designed in rather than retrofitted, and you will drive alignment on strategy and investment across multiple organizations without owning the teams that execute it. You will represent this product area to senior AWS leadership, enterprise CFOs and FinOps practitioners, and industry analysts, and you will shape how the market thinks about the economics of running AI in production.
This role requires deep technical judgment, comfort operating with incomplete information on problems that have no precedent, and the influence to move organizations you do not control. If you are excited about defining a product category that does not yet exist — and about the fact that every enterprise scaling generative AI will eventually need what you build — this role offers the opportunity to set AWS's position on the economics of AI.
Key job responsibilities
- Engage with customers through a variety of channels and serve as the voice of the customer internally.
- Gain feedback from customers to ensure right focus in building our products
- Manage the entire product life cycle from strategic planning to tactical execution.
- Partner with engineering teams to deliver on the product roadmap.
- Establish goals and review metrics to identify opportunities and measure the success of the product features.
- Provide effective written and verbal updates on the products and key projects to senior leadership and stakeholders.
BASIC QUALIFICATIONS
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- 5\+ years of working as a Technical Product Manager experience
- 3\+ years of technical (software development, network development, IT, other related) experience
- 5\+ years of creating written docs for development of new products experience
- 5\+ years of product management in the cloud computing technology space experience
- Bachelor's degree in computer science, engineering, math, finance, or economics
- Experience in taking a product from conception \& definition phase through engineering design and taking it to market
- Experience delivering large\-scale SaaS, PaaS or LaaS products where you are responsible for the full product lifecycle, from concept through GTM (go to market)
PREFERRED QUALIFICATIONS
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- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations
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 \- 152,200\.00 \- 205,900\.00 USD annually
Salary Context
This $152K-$205K range is below the median for AI Product Manager roles in our dataset (median: $185K across 167 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 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Amazon Web Services, 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 $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($179K) sits 18% below the category median. Disclosed range: $152K to $205K.
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 Web Services AI Hiring
Amazon Web Services has 93 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span New York, NY, US, Arlington, VA, US, Cupertino, CA, US. Compensation range: $160K - $350K.
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 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 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).
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 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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