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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 Hardware Engineer to join our GPU Accelerator Hardware team. In this role, you will own manufacturing stability and yield improvements, will engage from design phase through volume production and into fleet health. You will be hands\-on at the manufacturing line debugging complex system failures across hardware, firmware, and physical layers while driving manufacturing quality, test strategy, and process optimization.
You will serve as the primary engineering interface between hardware design teams, supply chain, and contract manufacturers (ODMs/CMs) — ensuring our products are optimized for manufacturability, launched with exceptional quality, and supported through production ramp.
Success in this role requires deep technical curiosity, willingness to personally solve the hardest problems at the line, structured problem\-solving, and strong cross\-functional communication.
Key job responsibilities
NPI Manufacturing Engineering
- Own end\-to\-end NPI manufacturing execution for GPU accelerator platforms across EVT/DVT/PVT builds and pilot production
- Perform NUDD risk assessments and carry\-over analysis to identify and mitigate manufacturing risks before design freeze
- Develop First Build Readiness Plans encompassing BOM availability, assembly strategy, test code readiness, and build instructions
- Define Manufacturing Test Strategy including test flows, coverage, fixture requirements (ICT, BFT, system\-level), and buy\-off criteria
- Lead DFx reviews (DFM/DFA/DFT) leveraging mechanical CAD models, gerber files, and mock\-up samples; drive design improvements with hardware engineering teams
- Create manufacturing capacity plans with line balance and cycle time analysis to maximize throughput
- Lead DFMEA/PFMEA reviews; develop process flow diagrams and quality control plans
- Develop Technical Manufacturing Information (TMI) packages for CM enablement
Hardware Debug \& Root Cause Analysis
- Debug complex system failures during manufacturing — diving deep across hardware, firmware, power, thermal, and signal integrity layers to drive root cause
- Perform root cause corrective action (RCCA) for manufacturing blockers, yield detractors, and quality escapes
- Correlate failure modes across firmware, kernel, driver, thermal, power, and physical layers
- Apply knowledge of server architecture (CPU, GPU, memory, NVMe, PCIe) to resolve integration and test failures at the manufacturing line
Quality, Automation \& Continuous Improvement
- Execute Test Rack Inspections and OK2Ship checklists; issue quality alerts, deviations, and ship\-holds as required
- Research automation techniques and develop new test systems to improve manufacturing efficiency and reduce manual intervention
- Drive toward predictive diagnostics using manufacturing telemetry, test data trending, and failure pattern analysis
- Identify systemic quality issues and drive design improvements in partnership with hardware engineering teams
Cross\-Team Collaboration
- Provide on\-site ODM/CM support during critical builds and production ramp
- Collaborate with internal teams on GPU module integration, thermal validation, and manufacturing requirements
- Partner with design engineering, operations, firmware, test, and qualification teams to close the loop between manufacturing issues and design improvements
- Manage manufacturing requirements, schedules, and deliverables across engineering teams, suppliers, and partners
A day in the life
Your day\-to\-day responsibilities include interfacing with internal 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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- Bachelor's degree or above in Electrical or Mechanical Engineering, or Bachelor's degree in Engineering, Mechanical Engineering, Operations, Supply Chain, or Business Administration
- 3\+ years of end to end product delivery experience
- 5\+ years of manufacturing or manufacturing engineering experience in server, accelerator, or high\-tech hardware platforms
- Hands\-on hardware debug experience — ability to root cause system\-level failures across electrical, firmware, and mechanical domains
- Experience with SMT assembly processes and PCB fabrication/assembly for HDI boards
- Experience driving quality and schedule execution at multinational ODMs/CMs
- Willingness to travel domestically and internationally (\~25%), including extended on\-site support during critical builds
PREFERRED QUALIFICATIONS
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- Master's degree in Electrical or Mechanical Engineering
- Experience working in a fast\-paced, rapidly changing operations environment
- Experience with GPU\-based server or accelerator platform manufacturing
- Familiarity with server technologies: PCIe topology, NVMe, high\-speed bus design, signal integrity, power distribution
- Experience with DFx and FMEA methodologies
- Experience building or improving manufacturing test systems and automation
- Experience with failure analysis techniques and fleet\-scale defect trending
- Knowledge of firmware, BIOS, BMC, and their interaction with manufacturing test flows
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, CO, Denver \- 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/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amazon.com, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($174K) sits 20% below the category median. Disclosed range: $136K to $212K.
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/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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