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About This Role
DESCRIPTION
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As a Cloud Hardware Development Engineer, you will be an end\-to\-end owner of Edge and/or Accelerator (AI/ML/GPU) Server Platforms, from New Product Introduction (NPI) through fleet health in production. You own the full lifecycle: design, development, qualification, launch, and ongoing operational excellence of servers running at scale in the AWS fleet.
You will work closely with internal customers to understand their technical needs and business goals, leveraging your experience with server design and the knowledge of various teams to architect solutions we deploy at scale. To deliver your products, you will work with an interdisciplinary team of component, firmware, power, mechanical, electrical, test, qualification, manufacturing engineers, and lead our ODM (design and manufacturing partners) to bring these servers to the data center. After launch, you own the fleet — monitoring quality, driving reliability improvements, and ensuring servers continue to meet customer requirements throughout their
operational life.
This role demands deep technical curiosity and the willingness to jump in and personally solve the hardest problems. When a complex system failure occurs — whether during NPI qualification or in a production fleet of hundreds of thousands of servers — you roll up your sleeves, dive into the details across hardware, firmware, software, and physical layers, and drive to root cause. You don't wait for someone else to figure it out.
You will own end\-to\-end system reliability — proactively identifying deficiencies and driving toward zero\-touch operations where automation detects, diagnoses, and resolves issues before customer impact. You will decompose complex server system problems (testability, reliability, diagnostics) into deliverable tasks and features, leading delivery yourself and through others in parallel.
This is a fast\-paced, intellectually challenging position. You'll work with thought leaders in multiple technology areas, hold high standards for yourself and everyone you work with, and constantly look for ways to improve your products' performance, quality, and cost. We're changing an industry, and we want individuals who are ready for this challenge and want to reach beyond what is possible today.
Key job responsibilities
NPI — New Product Introduction
- Own the end\-to\-end NPI lifecycle for Edge and/or Accelerator (AI/ML/GPU) Server Platforms — from architecture definition through design, qualification, manufacturing ramp, and launch
- Lead technical solutions for complex server and rack system architectural challenges
- Work with ODM/manufacturing partners to develop, validate, and manufacture server products at scale
- Develop functional specifications, design verification plans, and test procedures
- Drive qualification and readiness milestones, ensuring new platforms meet performance, reliability, and cost targets before fleet deployment
- Identify and resolve technical risks early in the development cycle — don't let problems reach production
Fleet Health, Diagnostics \& Automation
- Own fleet health for the server platforms you launch — reliability doesn't end at ship
- Design and implement predictive failure detection systems using telemetry, sensor data, error trending, and log correlation to identify hardware issues before they cause customer impact
- Drive toward zero\-touch operations — help build detection, diagnoses, and remediation of faults without human intervention
- Debug complex system failures in time\-sensitive settings — personally diving deep when the problem demands it
- Perform root cause analysis correlating across firmware, kernel, driver, thermal, power, and physical layers
Systems Design \& Technical Depth
- Apply expertise across hardware, software, system design, x86 architecture, processes, and operations (compute, storage, network, GPU)
- Design and implement solutions to address system\-level issues at large scale
- Decompose complex server system problems (testability, reliability, diagnostics) into deliverable tasks and features
- Collaborate with hardware, software, manufacturing, supply chain, and product management teams
Cross\-Team Collaboration
- Work closely with internal customers to ensure new server hardware meets data path and control path requirements
- Identify early any potential problems onboarding new servers into customer ecosystems
- Collaborate across Hardware Engineering, component, firmware, test, qualification, and integration teams
- Partner with datacenter operations to close the loop between field failures and design improvements
A day in the life
Your day\-to\-day responsibilities include interfacing with internal and external customers to understand product requirements and facilitate system development on top of your server designs. You will learn operational challenges facing our existing fleet with the goal of improving the current customer experience and developing improved systems for future designs. You will work directly with vendors and ODM (manufacture partners) to scale your product. Some days you're reviewing a new platform design with your ODM; other days you're deep in logs and telemetry data chasing a failure mode across the fleet. You thrive on that range.BASIC QUALIFICATIONS
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- Experience in developing functional specifications, design verification plans and functional test procedures
- Bachelor's degree or above in electrical engineering, computer engineering, or equivalent
- Experience in English\-language communication skills, both written and verbal
- Experience with design \& innovation and research \& development
- Knowledge of operating systems, hardware, storage, network, security, database administration and cloud infrastructure
- Experience in server technologies such as, thermal, mechanical, power, and signal integrity
- 5\+ years of professional work (non\-internship) experience
PREFERRED QUALIFICATIONS
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- 5\+ years of hardware design and validation of components, subsystems and systems experience
- Experience working with Advanced Compute technologies including, but not limited to: Accelerated Compute, High Performance Compute, Visual/Spatial Compute, and/or IoT.
- Experience in server technologies: board design, high\-speed bus design and signal integrity, failure analysis, server components (CPU, GPU, SSDs, memory), BIOS, BMC, and networking
- Experience developing and executing test procedures for mechanical and/or electrical systems/components
- Experience working with ODMs/manufacturer through the product development and manufacturing lifecycle
- Experience building predictive failure detection or proactive remediation systems at fleet scale
- Familiarity with PCIe topology, NVMe, and accelerator interconnects
- Experience with large\-scale datacenter or cloud environments
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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, CA, Cupertino \- 157,300\.00 \- 212,800\.00 USD annually
USA, TX, Austin \- 136,000\.00 \- 184,000\.00 USD annually
USA, WA, Seattle \- 136,000\.00 \- 184,000\.00 USD annually
Salary Context
This $136K-$212K 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.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 $217,100 based on 471 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($174K) sits 20% below the category median. Disclosed range: $136K to $212K.
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.com AI Hiring
Amazon.com has 122 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Product Manager, AI Software Engineer. Positions span Seattle, WA, US, Santa Clara, CA, US, New York, NY, US. Compensation range: $128K - $338K.
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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