Interested in this AI Product Manager role at Amazon.com?
Apply Now →Skills & Technologies
About This Role
DESCRIPTION
---------------
The AWS Neurosymbolic AI team is pioneering the integration of formal reasoning and neural approaches to build AI systems that are not only powerful, but provably correct. We sit at one of the most compelling frontiers in computer science: the convergence of neural networks and symbolic reasoning, where large language models meet theorem provers, and where probabilistic intelligence meets mathematical certainty.
Our mission is to make AI trustworthy at scale. We develop technology that enables AI systems to reason rigorously, verify their own outputs, and provide mathematical guarantees about their behavior. This is a fundamental shift in how AI systems are built, and we believe it's on the critical path to the next generation of safe, reliable AI\-powered applications.
We are one of the strongest concentrations of neurosymbolic AI talent in industry. Our team includes original contributors to the Lean theorem prover and is advised by Lean's Chief Architect. We bring together researchers and engineers from both the AI and formal methods communities, a combination that is extraordinarily rare and increasingly essential.
We build on Amazon's 10\+ year track record of bringing automated reasoning to production at scale. AWS pioneered the use of formal methods in cloud infrastructure, from network reachability analysis to cryptographic protocol verification to access policy reasoning, systems that serve hundreds of millions of customers today. Now we're taking the next giant leap: fusing that heritage with frontier AI to make every AI system verifiable, trustworthy, and safe. The science innovations developed by this team already power products in customers' hands: Automated Reasoning Checks in Amazon Bedrock Guardrails, policy verification in Amazon Bedrock AgentCore, and intelligent specification, testing, and correctness workflows in Kiro.
We publish at top venues, collaborate with leading academic institutions, and operate with the urgency and ownership of a startup inside one of the world's most impactful technology companies.
If you're excited by the idea of teaching machines to prove, not just predict, we'd love to talk.
What we're building
We are building a platform that brings the rigor of formal mathematics to the world of AI and software development. Our technology enables developers, AI agents, and autonomous systems to formally verify correctness, enforce guarantees, and establish trust, especially as AI\-generated code and autonomous agents become the default, not the exception.
The core question we're answering: as AI systems become more capable and more autonomous, how do you know they did what you asked, correctly, safely, and completely? We're building the answer, using technologies like Lean 4 (the same formal language behind recent breakthroughs in AI mathematical reasoning) combined with state\-of\-the\-art neural approaches.
Our platform combines neural networks with formal verification engines, enabling capabilities that neither approach achieves alone: AI that writes code and proves it's correct. Agents that act autonomously and guarantee they'll respect constraints. Systems that reason about their own behavior with mathematical precision.
This is early, high\-impact work with direct visibility to AWS's most senior leaders. The customers you'll serve span from Fortune 100 enterprises betting their businesses on AI, to the developer communities building the next generation of autonomous software. You'll be shaping products that define how the world builds trustworthy AI for the next decade.
Key job responsibilities
The Role
As a Technical Product Manager on the AWS Neurosymbolic AI team, you will own the feature definition, customer engagement, and delivery for a focused product area within our neurosymbolic AI platform. You'll work closely with scientists and engineers to turn capabilities into features that customers love — defining requirements, running betas, synthesizing feedback, and driving iterative delivery.
You'll be the connective tissue between what we can build and what customers need. That means spending real time with customers understanding their workflows and pain points, writing crisp product specs that engineering can execute against, and ensuring that what ships actually solves the problem. You should be comfortable going deep on technical details — understanding formal specifications, verification workflows, or managed service architectures — while always keeping the customer experience front and center.
Key Responsibilities:
- Own feature definition and delivery for a specific product area within the neurosymbolic AI platform
- Engage directly with customers and beta participants to understand needs, gather feedback, and validate solutions
- Write clear product requirements and specifications that translate customer problems into buildable scope
- Drive prioritization within your area — making tradeoffs between customer asks, technical debt, and new capabilities
- Partner closely with engineering and science teams through the full development lifecycle
- Define and track success metrics for your features and surface learnings to inform broader product direction
- Contribute to launch readiness — documentation, enablement materials, and go\-to\-market support
- Communicate progress, blockers, and customer insights to leadership and cross\-functional partners
About the team
Who Thrives Here: We're looking for people who defy easy categorization. Engineers who think like product managers. Scientists who care about shipping. Product leaders who can read a paper and sketch a system architecture on a whiteboard. The problems we're solving require people who move fluidly between disciplines, and we've built a culture that rewards breadth as much as depth.
Inclusive Team Culture: The best ideas at the intersection of AI and formal reasoning come from people with different backgrounds and training: mathematicians who became engineers, systems programmers who fell in love with type theory, researchers who wanted more than approximate answers. If your path has been nonlinear, you'll fit right in. We actively seek a diversity of perspectives because the problems demand it.
Training \& Career Growth: You'll work alongside an industry\-leading team of scientists and engineers who are defining a new field. We invest in growth, attending and publishing at top conferences, collaborating with university research partners, and creating the space to go deep on genuinely hard problems. This is a team where you'll learn constantly, from colleagues who are among the best in the world at what they do.
Work/Life Balance: Deep thinking requires rest, recovery, and a life outside of work. Flexible work arrangements are part of our culture, and we trust our team to manage their time and energy. When we feel supported in the workplace and at home, there's nothing we can't achieve.
Why Join Now? The convergence of large language models and formal reasoning is happening now. It will reshape how software is built, verified, and trusted, and we have the heritage, the talent, and the backing of AWS to lead it. The people who join this team today will define this field for years to come.BASIC QUALIFICATIONS
------------------------
- Bachelor's degree in Computer Science, Engineering, or a related technical field
- 5\+ years of technical product or program management experience
- 2\+ years of enterprise scale infrastructure or development\-based cloud programs/projects in a related industry experience
- Experience driving feature delivery end\-to\-end — from requirements through launch
- Strong written communication — you can write a product spec that engineers want to build from
PREFERRED QUALIFICATIONS
----------------------------
- Experience in identifying a customer need, building a product to meet that need, and launching that product
- Experience shipping innovative, successful consumer products
- Master's degree in a technical field
- Experience with developer tools, APIs, or platform services
- Familiarity with formal methods, compilers, programming languages, or verification tools
- Comfort working alongside scientists and engineers on technically complex products
- Experience with usage metrics, A/B testing, or data\-driven product decisions
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, MA, Boston \- 152,200\.00 \- 205,900\.00 USD annually
USA, NY, New York \- 167,400\.00 \- 226,500\.00 USD annually
Salary Context
This $167K-$226K range is above the median for AI Product Manager roles in our dataset (median: $188K across 140 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 3,708 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 $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($196K) sits 9% below the category median. Disclosed range: $167K to $226K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Amazon.com AI Hiring
Amazon.com has 97 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Sunnyvale, CA, US, Culver City, CA, US, San Francisco, CA, US. Compensation range: $97K - $327K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.