Interested in this AI Product Manager role at Amazon Web Services?
Apply Now →Skills & Technologies
About This Role
DESCRIPTION
---------------
As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day\-to\-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon’s real\-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no\-brainers to buy and easy to use.
We are looking for a single\-threaded leader to own an agent\-first design system end\-to\-end: product vision, roadmap, engineering execution, adoption, and business outcomes. This is a builder\-leader role. You will lead a team of PM, SDEs, MLEs, and front\-end engineers, and partner closely with the AAIS UX Design organization — design and creative direction, and the design team itself, sit with the UX design leader; you own prioritization, delivery, and the business results.
The design system you will own rethinks how humans interact with AI. Rather than bolting conversational agents onto legacy interfaces, it places the human\-AI relationship at the center of the design. It is built on the philosophy, called humorphism, that if people are going to work alongside AI teammates the way they work with human teammates, the interaction layer should be designed around how people have collaborated in work settings for decades, not around how they've clicked through software.
The work spans both proven agentic patterns and experimental approaches. Some components and patterns are ready for product teams to consume today. Others are on an innovation track where new AI\-native interaction paradigms get invented before they are ready to be productized.
Adoption is a first\-class workstream: making the design system easy for teams to adopt, resolving their blockers, and building the engineering relationships across AAIS and beyond.
You will set the roadmap, manage cross\-functional stakeholders across AAIS and partner teams, and personally contribute to the technical direction of the system. You must be design\-obsessed — someone with genuine taste who believes delight is a differentiator for any product experience — and comfortable operating at the intersection of design craft, front\-end engineering, and product strategy.
You will be an engaged creative partner to the design organization: bringing a strong point of view, disagreeing and committing, and diving into the design conversation as readily as the engineering one, while leaving the design work itself to the designers. In its current phase of development, the role will require strong communication, product\-leadership partnership, and research\-grounded decisions.
Key job responsibilities
- Own the product vision and roadmap. Define what the design system builds next based on portfolio needs, customer feedback, and design research. Balance near\-term adoption with long\-term architectural bets (e.g., confidence/uncertainty primitives, earned context, reasoning transparency).
- Design the best, for the most, for the least. Do the best work for the most customers for the least specialized effort — every unit of work should reach a large number of people, not one targeted product. Resist one\-off asks from individual apps; reframe them into patterns that generalize. Stop at doing the work for a team — do it with them and for everyone. Build coherence across the experience layer and a felt sense of product family, not siloed one\-off features. This is the systems\-thinking bar for the whole team.
- Balance velocity with room to explore. Be a force for the team's momentum, and plan deliberately for exploration where it counts — research, inspiration, and critique — so the hardest problems get the depth they deserve instead of being rushed. Know when a problem earns many explorations and when one will do.
- Lead the "Own It. Ship It. Together." collaboration model. The team operates through a paired model — named engineer \+ designer from day one, building simultaneously rather than sequentially. Sustain and scale this model as more teams adopt the design system.
- Drive platform adoption without mandates. The guiding principle is that adoption is earned through usefulness, not mandates. Grow adoption by releasing incrementally, validating with research, generalizing proven solutions, and directly supporting the engineering teams who take a dependency on the design system across AAIS and beyond.
- Steward the shared design system partnership. Own the integration layer that adapts proven patterns for product team consumption, and the working relationship that brings durable patterns into the shared AWS design system for broad adoption.
- Shape the long\-lead investments that support builders. Determine the longer\-horizon bets the design system needs to make to support how builders will work next — agents that compose UI, MCP\-based integration, mobile\-native, voice and multimodal. Own the roadmap for these before they're urgent.
- Partner with the design leader to hire, steer, and develop the team. The UX designers sit within the AAIS UX design organization; the front\-end engineers sit with you. Partner closely with the design leader to hire, develop, and retain world\-class talent at the intersection of design systems and agentic AI.
- Set and maintain a single quality bar. Establish governance for prioritization, release cadence, documentation standards, and design\-token management — in partnership with design on the craft bar and owning the business and delivery bar yourself.
What Success Looks Like (First 12 Months)
- Design system adoption grows from 3 AAIS products to portfolio\-wide coverage, with measurable reduction in per\-team UX build cost.
- The design system's full offering is available to the Connect family at 65% or more through the shared design system — and key workflows are unlocked.
- The experimental innovation track is established, with early AI\-native prototypes and a scoped set of long\-lead builder investments (composing agents, MCPs, multimodal).
- Team health and retention remain strong through a smooth transition from the current interim structure.
- Strategic roadmap items (confidence primitives, earned context, reasoning transparency) have defined requirements and early prototypes.
About the team
ABOUT AWS:
Diverse Experiences
Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Work/Life Balance
We value work\-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee\-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity and AmazeCon conferences, inspire us to never stop embracing our uniqueness.
Mentorship and Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge\-sharing, mentorship and other career\-advancing resources here to help you develop into a better\-rounded professional.
BASIC QUALIFICATIONS
------------------------
- Bachelor's degree in Computer Science or a related field
- 8\+ years of technical product management experience.
- 5\+ years of people management experience with direct responsibility for hiring, performance management, and team development.
- Deep expertise in design systems, front\-end engineering, or developer experience platforms.
- Track record of shipping developer\-facing SDKs, component libraries, or platform products used by multiple teams.
- Strong technical fluency — able to make architecture decisions, review code, and engage deeply with engineers and designers on implementation tradeoffs.
- A strong sense of craft and taste, and a desire to raise the bar — willing to invest more effort now for work that scales better later, rather than always optimizing for speed.
PREFERRED QUALIFICATIONS
----------------------------
- Experience managing a team of engineers and scientists
- Experience building for AI/ML or agentic applications — understanding how LLM\-driven interfaces differ from traditional UIs.
- Interest in, or experience with, how design systems adapt to vibe coding and agent\-readable design systems is a strong plus.
- Genuine enthusiasm for design and visual culture — you care how a product feels to use, not just whether it works, and you can articulate why you prefer one well\-made product over another.
- Demonstrated ability to manage a horizontal platform team serving multiple product consumers with competing priorities.
- Experience with the React ecosystem, Figma design tooling, and design token pipelines.
- Background in human\-computer interaction, interaction design, or UX engineering.
- Familiarity with AWS services and the Cloudscape design system.
- Comfort operating in a fast\-moving, highly ambiguous environment where the team is inventing new interaction paradigms for agentic AI.
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 \- 219,100\.00 \- 296,500\.00 USD annually
USA, WA, Seattle \- 199,200\.00 \- 269,500\.00 USD annually
Salary Context
This $199K-$296K range is above the 75th percentile 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 ($247K) sits 14% above the category median. Disclosed range: $199K to $296K.
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 New York pay a median of $220,000 across 1,650 tracked positions.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.