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About This Role
DESCRIPTION
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Do you want to build the backbone of Generative AI at AWS? Do you want to build the future of the cloud for AI training and inference, delivering continuous price performance improvements for multi\-billion variable LLMs at cloud scale? Come join us. We are seeking a Systems Development Engineer to develop automation software, diagnostic tooling, and fleet health infrastructure for our accelerated (AI/ML) server platforms. You will work across multiple teams and organizations to build scalable, reliable systems that keep our fleet healthy — with a vision toward zero\-touch operations where automation detects, diagnoses, and resolves issues without human intervention.
What You Will Do
You will solve complex architectural problems that may not be well\-defined in advance. You will own your team's systems, proactively identify deficiencies, and write scalable, robust code to solve issues before they impact customers. You will decompose large, difficult server testability, reliability, and diagnosis problems into straightforward tasks and components — delivering yourself and through others in parallel — using a combination of hardware, software, system design, processor architecture, diagnostics, and operations knowledge.
Key job responsibilities
Fleet Health \& Predictive Infrastructure
1\. Build and own the automation infrastructure responsible for the health of the accelerator (AI/ML) compute server fleet
2\. 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
3\. Drive toward zero\-touch operations — building automation that detects, diagnoses, triages, and remediates hardware and software faults without human intervention
4\. Develop monitoring tools, dashboards, and alerting systems to provide real\-time visibility into fleet health across lab and production environments
5\. Define and track fleet health metrics (failure rates, mean time to detect, mean time to repair, first\-time fix rate, predictive accuracy)
Debugging \& Troubleshooting
1\. Debug and resolve complex system\-level issues across compute, GPU, and networking in production environments
2\. Troubleshoot Linux boot and runtime failures across x86 and ARM architectures, including PCIe, power, NIC, NVMe, and GPU subsystems
3\. Perform root cause analysis on hardware failures — correlating across firmware, kernel, driver, and physical layer to isolate faults
4\. Build diagnostic tooling that automates root cause identification and reduces reliance on manual triage
5\. Improve manufacturing throughput and yield through test optimization
Systems Development \& Automation
1\. Define and develop software, automation, and enabling tools for server hardware programs; track and report progress
2\. Design and build scalable system\-level software with focus on durability, availability, security, and diagnostics
3\. Develop and maintain device drivers for Linux on ARM and x86 architectures
4\. Build automation solutions using modern programming languages (Python, Ruby, Java, C/C\+\+, etc.)
5\. Work with OS internals and accelerator/GPU software stacks in Linux\-based environments
6\. Build, manage, and deploy CI/CD pipelines for rapid deployment of code changes to org\-owned and customer\-owned systems
Cross\-Team Collaboration
1\. Work across internal HWEng teams to ensure new server hardware addresses data path and control path functionality needed by dependent service teams
2\. Work closely with internal customers to identify early any potential problems onboarding new accelerated compute servers into their ecosystem
3\. Engage with ODMs and design partners on testability, diagnostic, and automation requirements during hardware design and development (NPI)
4\. Contribute to server design to improve robustness, testability, diagnosability, and reliability
5\. Partner with datacenter operations teams to close the loop between field failures and design improvements
A day in the life
You will collaborate with a variety of roles (SDEs, SDETs, Mechanical/Electrical/Hardware Engineers, TPMs, Managers, Principals) and organizations through server conception, test validation, qualification, launch, and operations — driving high quality and reliability into current and future designs for AWS accelerated server solutions. From orchestration tooling development to hardware integration to kernel driver debugging, you dive deep into problems across the breadth of AWS.
About the team
The Hardware Engineering AI/ML development team is a group of engineers and technical program managers directly responsible for launching and maintaining server hardware in the fleet — including AI/ML accelerator servers with GPUs. Located in Seattle, Cupertino, and Austin, we work with internal development teams, ODMs, and design partners to deliver servers deployed in datacenters worldwide.
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
- 3\+ years of non\-internship professional software development experience, or Bachelor's degree or above in computer science or equivalent
- 3\+ years of systems design, software development, operations, automation, and process improvement experience
- 3\+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience
- 3\+ years of programming with at least one modern language such as C\+\+, C\#, Java, Python, Golang, PowerShell, Ruby experience
- 2\+ years of Linux operating systems experience, or experience in a relevant field (e.g., Computer Science, Networking Engineering)
- Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems, or experience building complex software systems that have been successfully delivered to customers
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
- Experience building complex software systems that have been successfully delivered to customers, or experience in computer architecture
- 3\+ years of conducting predictive and preventative maintenance procedures experience
- Experience in Linux OS and network troubleshooting, or experience in software development and experience demonstrating software engineering skills in a previous intership, work experience, coding competitions, or publications
- Experience debugging, profiling, and implementing best software engineering practices in large\-scale systems, or experience with CUDA kernels or ML/low\-level kernels
- Experience completing complex tasks quickly with little to no guidance and react with appropriate urgency to situations that require a quick turnaround, or experience in a fast\-paced, high\-tech company
- Familiarity with server hardware architecture, BMC/IPMI, firmware, PCIe topology, and hardware diagnostics
- Experience working with ODMs or hardware design partners
- Exposure to zero\-touch or self\-healing automation concepts for large\-scale infrastructure
- Experience working in large\-scale datacenter or cloud environments
- Experience with hardware bring\-up, validation, or fleet\-wide deployment
- Familiarity with telemetry pipelines, anomaly detection, or operational metrics at scale
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 \- 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 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 $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 Web Services AI Hiring
Amazon Web Services has 73 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, Data Scientist. Positions span New York, NY, US, Austin, TX, US, Jersey City, NJ, US. Compensation range: $129K - $342K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% 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 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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