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About This Role
DESCRIPTION
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Application deadline: Jul 22, 2026
Amazon Web Services (AWS) Hardware Engineering designs and delivers next\-generation cloud infrastructure. Our team builds custom accelerator systems that power AI, machine learning, and compute workloads at global scale.
We are seeking a Manufacturing Systems Development Engineer to own manufacturing test software, diagnostic tooling, and hardware debug for GPU\-based server platforms at our ODM/CM manufacturing sites. In this role, you will be on the manufacturing floor debugging complex system failures, developing automation to improve yield and throughput, and building diagnostic tools that enable root cause identification at the line. You will bridge the gap between hardware design intent and manufacturing execution — ensuring our platforms are testable, diagnosable, and launch with exceptional quality.
This role requires someone equally comfortable writing code and debugging hardware. You will develop test automation, build diagnostic frameworks, and personally troubleshoot failures spanning firmware, kernel, drivers, PCIe, power, and GPU subsystems — all in a fast\-paced manufacturing environment. When something fails at the line, you are the person who figures out why.
Domestic and international travel (\~25%)
Key job responsibilities
Manufacturing Debug \& Root Cause Analysis
- Debug complex system\-level failures at the manufacturing line across compute, storage, GPU, networking, power, and thermal domains
- Perform root cause analysis correlating across firmware, kernel, driver, PCIe, signal integrity, and physical layers to isolate faults
- Troubleshoot Linux boot and runtime failures across x86 and ARM architectures, including NVMe, GPU, NIC, and accelerator subsystems
- Drive Root Cause Corrective Action (RCCA) for yield detractors, test escapes, and recurring manufacturing failures
- Provide on\-site ODM/CM support during critical builds, EVT/DVT/PVT phases, and production ramp
Test Software \& Automation Development
- Design, develop, and maintain manufacturing test software and diagnostic tools deployed at ODM/CM lines
- Build automation that reduces manual triage — enabling faster fault isolation and higher first\-pass yield
- Develop and optimize system\-level test flows (BFT, functional test, stress test, burn\-in) for GPU accelerator platforms
- Build, manage, and deploy CI/CD pipelines for rapid deployment of test code to manufacturing environments
- Write scalable, robust code in Python, C/C\+\+, or Java to solve manufacturing test and debug challenges
Manufacturing Process \& Quality
- Define and improve manufacturing test strategy including coverage, duration, fixture requirements, and pass/fail criteria
- Analyze test data and yield trends to identify systemic issues; drive design and process improvements
- Collaborate on DFx reviews (DFT/DFM) to ensure new designs are testable and diagnosable at the manufacturing line
- Develop diagnostic tooling requirements for ODM/CM enablement — ensuring partners can effectively screen and debug at scale
- Research and implement automation techniques to improve manufacturing efficiency and reduce human intervention
Cross\-Team Collaboration
- Work across hardware design, firmware, qualification, and manufacturing engineering teams to close the loop between line failures and design improvements
- Engage with ODMs and design partners on testability, diagnostic, and automation requirements during NPI
- Collaborate with internal teams on GPU module integration, test coverage, and manufacturing debug procedures
- Partner with fleet health teams to ensure manufacturing diagnostics align with production monitoring and field failure analysis
BASIC QUALIFICATIONS
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- 2\+ years of non\-internship professional software development experience
- 1\+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience
- Experience programming with at least one modern language such as C\+\+, C\#, Java, Python, Golang, PowerShell, Ruby
- 5\+ years of software development experience with at least one modern language (Python, C/C\+\+, Java)
- 3\+ years of experience debugging hardware systems — server, accelerator, storage, or high\-tech platforms
- Experience with Linux/Unix systems including boot flow, kernel, drivers, and OS\-level diagnostics
- Hands\-on experience troubleshooting hardware failures at a manufacturing line or lab environment
- Experience working with ODMs/CMs through product development and manufacturing lifecycle
- Willingness to travel domestically and internationally (\~25%), including extended on\-site manufacturing support
PREFERRED QUALIFICATIONS
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- Experience with GPU\-based server or accelerator platform manufacturing and debug
- Familiarity with server hardware architecture: PCIe topology, NVMe, BMC/IPMI, power delivery, thermal
- Experience developing manufacturing test automation or diagnostic frameworks at scale
- Experience with board\-level debug (oscilloscope, logic analyzer)
- Knowledge of firmware, BIOS, BMC, and their interaction with manufacturing test flows
- Experience with manufacturing yield analysis, test optimization, and throughput improvement
- Experience building CI/CD pipelines for test software deployment
- Familiarity with telemetry, log correlation, and failure pattern analysis in manufacturing 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, CO, Denver \- 129,200\.00 \- 174,800\.00 USD annually
USA, WA, Seattle \- 129,200\.00 \- 174,800\.00 USD annually
Salary Context
This $129K-$174K range is below 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($152K) sits 30% below the category median. Disclosed range: $129K to $174K.
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 Denver pay a median of $201,050 across 48 tracked positions. That's 8% below 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 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
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