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Senior Technical Program Manager, AWS Generative AI & ML Servers
Taipei City, TWN
Actively hiring, first seen by us 5 hours ago- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: full-time
From the employer’s posting
Education & alternatives
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent - 6+ years of technical product or program management experience, working directly with multiple engineering teams. - 6+ years of experience driving hardware development programs (servers, racks, networking, or storage) through full product lifecycle including design, validation, manufacturing, and fleet deployment - Experience managing programs across cross-functional teams, building processes, and coordinating release schedules Preferred qualifications - Master's degree in Computer Science, Electrical Engineering, or a related field - 5+ years of experience coordinating complex server programs with ODM/JDM partners across multiple geographies, managing design reviews, manufacturing readiness gates, and quality checkpoints - 5+ years of experience working with GPU/accelerator server platforms, NPI (New Product Introduction), or hardware qualification programs across various phases (EVT, DVT, PVT) - Experience driving root cause analysis and corrective action processes for hardware reliability issues at fleet scale - Knowledge of server hardware development lifecycle: electrical/mechanical design, firmware, thermal/power validation, and manufacturing testing. - Experience driving zero-touch automation or fleet health programs for large-scale infrastructure - Experience communicating engineering trade-offs and technical risks in business terms to leadership 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.
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 13, 2026today
- Recorded sightings
- 1
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
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- AWS
Where the posting names them
AWS operates the world's largest fleet of GPU-accelerated servers powering AI/ML workloads at cloud scale. Our team designs, builds, and operates this fleet — solving systemic hardware issues and building systems that detect and prevent recurrence so customers experience the highest quality of service. We are seeking a Senior Technical Program Manager to drive end-to-end delivery of GPU-accelerated servers across our global fleet. You will coordinate cross-functional engineering teams spanning hardware, firmware, and software, manage ODM partnerships across multiple continents, and establish closed-loop quality systems that drive continuous improvements. This role requires technical depth to translate engineering constraints into program risk, combined with program management excellence to deliver complex hardware at global scale.
A day in the life You start the day syncing with ODM partners across time zones on build status and open engineering actions. Mid-morning, you run an engineering review connecting firmware, software, and hardware teams to unblock a qualification gate. In the afternoon, you triage a fleet reliability issue with operations data, drive alignment on corrective actions, and update executive stakeholders on program risk posture. You end the day reviewing NPI milestone readiness and ensuring the next design review has clear entry criteria. About the team The Hardware Engineering AI/ML UltraServer platform team is a group of engineers and technical program managers directly responsible for launching GPU-accelerated servers into the AWS fleet. Located in Seattle, Austin, and Cupertino, we collaborate with global development teams and ODM partners to deliver next-generation AI/ML infrastructure deployed in datacenters worldwide. We move fast with small, empowered teams delivering end-to-end — from server conception through fleet-scale operations. Basic qualifications - Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent - 6+ years of technical product or program management experience, working directly with multiple engineering teams. - 6+ years of experience driving hardware development programs (servers, racks, networking, or storage) through full product lifecycle including design, validation, manufacturing, and fleet deployment - Experience managing programs across cross-functional teams, building processes, and coordinating release schedules Preferred qualifications
Your next step
Complete your application on www.amazon.jobs. The employer’s form will show what is required.
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Has Amazon posted this job before?
Yes. We’ve followed one other Senior Technical Program Manager, AWS Generative AI & ML Servers job there since November 2026. It is still up.
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
AWS operates the world's largest fleet of GPU-accelerated servers powering AI/ML workloads at cloud scale. Our team designs, builds, and operates this fleet — solving systemic hardware issues and building systems that detect and prevent recurrence so customers experience the highest quality of service.
We are seeking a Senior Technical Program Manager to drive end-to-end delivery of GPU-accelerated servers across our global fleet. You will coordinate cross-functional engineering teams spanning hardware, firmware, and software, manage ODM partnerships across multiple continents, and establish closed-loop quality systems that drive continuous improvements. This role requires technical depth to translate engineering constraints into program risk, combined with program management excellence to deliver complex hardware at global scale.
What You Will Do
You will own programs where the critical path runs through silicon, firmware, and software teams simultaneously. You will translate ambiguity into structure: turning a fleet telemetry signal into a corrective action plan with quantified failure rates, a customer requirement into a new platform milestone with EVT/DVT/PVT gates, or a manufacturing escape into a design change with updated validation criteria. You will drive decisions on program trade-offs — adjusting scope when qualification gates slip, balancing deployment speed against fleet risk, and determining when to accept interim mitigations instead of holding for root-cause fixes. When a large scale of GPU servers depend on your program landing on time, you are the one ensuring hardware readiness, qualification completeness, and operational handoff happen without gaps.
Why You Will Love It
The world's most advanced frontier models are trained on the platforms you help build. Your programs launch the GPU servers that power the largest AI/ML workloads on the planet. You will see your decisions reflected in fleet reliability metrics within weeks of deployment. The team is small and high-trust — you own programs end to end from concept through production, with direct access to leadership and engineering alike.
The Ideal Candidate
You have deep technical intuition across hardware and software — enough to challenge engineering decisions, not just track them. You thrive in ambiguity, bringing structure to programs where requirements, timelines, and dependencies are still forming. You align priorities across teams in different organizations, and you escalate with data, not noise. You actively mentor and develop others — TPMs and engineers alike. You contribute to hiring, promotion assessments, and raising the bar for program management practices in your organization.
Key job responsibilities
Strategy & Mechanisms
Define program strategy, objectives, and success criteria; influence resource allocation and priority decisions across engineering workstreams to align with organizational goals
Build and own mechanisms for program visibility — defining metrics, dashboards, and review cadences that enable data-driven decisions and early risk detection
Streamline delivery processes across teams; identify and eliminate dependencies, redundant gates, or coordination overhead that slow velocity
Requirements & Planning
Facilitate requirements gathering with internal customers; develop Technical Requirements Documents (TRDs) covering server specs, rack configurations, PCIe topology, power/cooling topology, and SKU definitions
Build program timelines aligned to different phases (Program Initiation, Design, Qualification, Pilot, Post-launch) with critical path analysis, risk identification with new & unique changes to the hardware, and milestone tracking for the program.
Drive Program Initiation reviews: scope definition, preliminary annualized failure rate predictions, resource planning, supply chain long-lead identification, and RFP (Request for Proposal) issuance to ODMs
Execution & Coordination
Drive cross-functional alignment across hardware, firmware (BIOS, BMC, CPLD), software, and operations teams through design reviews, manufacturing readiness and production readiness for fleet deployment.
Manage ODM partnerships: track EVT/DVT/PVT builds, manufacturing readiness gates, Bill of Materials (BOM) management in PLM systems, and quality checkpoints
Identify blockers early, escalate dependencies before they impact critical path, and facilitate technical trade-off decisions across engineering workstreams.
Communicate program status to leadership with clear reporting on milestone progress, risk posture, and mitigation plans.
Risk & Quality
Challenge technical workstreams to surface risks early; quantify impact to schedule and reliability (annualized failure rate targets, availability SLAs) with proposed mitigations
Drive root cause analysis of fleet-wide hardware failures and ensure corrective actions flow back into qualification criteria and design requirements
Define acceptance criteria, coordinate qualification testing at server and rack levels, and manage go/no-go decisions for mission-critical AI/ML workloads
Transition
Conduct knowledge transfer and document lessons learned; ensure operational readiness including automation, monitoring, and runbook completeness for production handoff
May require occasional (<10%) regional and international travel to Design and Manufacturing Partner sites.
A day in the life
You start the day syncing with ODM partners across time zones on build status and open engineering actions. Mid-morning, you run an engineering review connecting firmware, software, and hardware teams to unblock a qualification gate. In the afternoon, you triage a fleet reliability issue with operations data, drive alignment on corrective actions, and update executive stakeholders on program risk posture. You end the day reviewing NPI milestone readiness and ensuring the next design review has clear entry criteria.
About the team
The Hardware Engineering AI/ML UltraServer platform team is a group of engineers and technical program managers directly responsible for launching GPU-accelerated servers into the AWS fleet. Located in Seattle, Austin, and Cupertino, we collaborate with global development teams and ODM partners to deliver next-generation AI/ML infrastructure deployed in datacenters worldwide. We move fast with small, empowered teams delivering end-to-end — from server conception through fleet-scale operations.
- 6+ years of technical product or program management experience, working directly with multiple engineering teams.
- 6+ years of experience driving hardware development programs (servers, racks, networking, or storage) through full product lifecycle including design, validation, manufacturing, and fleet deployment
- Experience managing programs across cross-functional teams, building processes, and coordinating release schedules
- 5+ years of experience coordinating complex server programs with ODM/JDM partners across multiple geographies, managing design reviews, manufacturing readiness gates, and quality checkpoints
- 5+ years of experience working with GPU/accelerator server platforms, NPI (New Product Introduction), or hardware qualification programs across various phases (EVT, DVT, PVT)
- Experience driving root cause analysis and corrective action processes for hardware reliability issues at fleet scale
- Knowledge of server hardware development lifecycle: electrical/mechanical design, firmware, thermal/power validation, and manufacturing testing.
- Experience driving zero-touch automation or fleet health programs for large-scale infrastructure
- Experience communicating engineering trade-offs and technical risks in business terms to leadership
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.
null
We are seeking a Senior Technical Program Manager to drive end-to-end delivery of GPU-accelerated servers across our global fleet. You will coordinate cross-functional engineering teams spanning hardware, firmware, and software, manage ODM partnerships across multiple continents, and establish closed-loop quality systems that drive continuous improvements. This role requires technical depth to translate engineering constraints into program risk, combined with program management excellence to deliver complex hardware at global scale.
What You Will Do
You will own programs where the critical path runs through silicon, firmware, and software teams simultaneously. You will translate ambiguity into structure: turning a fleet telemetry signal into a corrective action plan with quantified failure rates, a customer requirement into a new platform milestone with EVT/DVT/PVT gates, or a manufacturing escape into a design change with updated validation criteria. You will drive decisions on program trade-offs — adjusting scope when qualification gates slip, balancing deployment speed against fleet risk, and determining when to accept interim mitigations instead of holding for root-cause fixes. When a large scale of GPU servers depend on your program landing on time, you are the one ensuring hardware readiness, qualification completeness, and operational handoff happen without gaps.
Why You Will Love It
The world's most advanced frontier models are trained on the platforms you help build. Your programs launch the GPU servers that power the largest AI/ML workloads on the planet. You will see your decisions reflected in fleet reliability metrics within weeks of deployment. The team is small and high-trust — you own programs end to end from concept through production, with direct access to leadership and engineering alike.
The Ideal Candidate
You have deep technical intuition across hardware and software — enough to challenge engineering decisions, not just track them. You thrive in ambiguity, bringing structure to programs where requirements, timelines, and dependencies are still forming. You align priorities across teams in different organizations, and you escalate with data, not noise. You actively mentor and develop others — TPMs and engineers alike. You contribute to hiring, promotion assessments, and raising the bar for program management practices in your organization.
Key job responsibilities
Strategy & Mechanisms
Define program strategy, objectives, and success criteria; influence resource allocation and priority decisions across engineering workstreams to align with organizational goals
Build and own mechanisms for program visibility — defining metrics, dashboards, and review cadences that enable data-driven decisions and early risk detection
Streamline delivery processes across teams; identify and eliminate dependencies, redundant gates, or coordination overhead that slow velocity
Requirements & Planning
Facilitate requirements gathering with internal customers; develop Technical Requirements Documents (TRDs) covering server specs, rack configurations, PCIe topology, power/cooling topology, and SKU definitions
Build program timelines aligned to different phases (Program Initiation, Design, Qualification, Pilot, Post-launch) with critical path analysis, risk identification with new & unique changes to the hardware, and milestone tracking for the program.
Drive Program Initiation reviews: scope definition, preliminary annualized failure rate predictions, resource planning, supply chain long-lead identification, and RFP (Request for Proposal) issuance to ODMs
Execution & Coordination
Drive cross-functional alignment across hardware, firmware (BIOS, BMC, CPLD), software, and operations teams through design reviews, manufacturing readiness and production readiness for fleet deployment.
Manage ODM partnerships: track EVT/DVT/PVT builds, manufacturing readiness gates, Bill of Materials (BOM) management in PLM systems, and quality checkpoints
Identify blockers early, escalate dependencies before they impact critical path, and facilitate technical trade-off decisions across engineering workstreams.
Communicate program status to leadership with clear reporting on milestone progress, risk posture, and mitigation plans.
Risk & Quality
Challenge technical workstreams to surface risks early; quantify impact to schedule and reliability (annualized failure rate targets, availability SLAs) with proposed mitigations
Drive root cause analysis of fleet-wide hardware failures and ensure corrective actions flow back into qualification criteria and design requirements
Define acceptance criteria, coordinate qualification testing at server and rack levels, and manage go/no-go decisions for mission-critical AI/ML workloads
Transition
Conduct knowledge transfer and document lessons learned; ensure operational readiness including automation, monitoring, and runbook completeness for production handoff
May require occasional (<10%) regional and international travel to Design and Manufacturing Partner sites.
A day in the life
You start the day syncing with ODM partners across time zones on build status and open engineering actions. Mid-morning, you run an engineering review connecting firmware, software, and hardware teams to unblock a qualification gate. In the afternoon, you triage a fleet reliability issue with operations data, drive alignment on corrective actions, and update executive stakeholders on program risk posture. You end the day reviewing NPI milestone readiness and ensuring the next design review has clear entry criteria.
About the team
The Hardware Engineering AI/ML UltraServer platform team is a group of engineers and technical program managers directly responsible for launching GPU-accelerated servers into the AWS fleet. Located in Seattle, Austin, and Cupertino, we collaborate with global development teams and ODM partners to deliver next-generation AI/ML infrastructure deployed in datacenters worldwide. We move fast with small, empowered teams delivering end-to-end — from server conception through fleet-scale operations.
Basic qualifications
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent- 6+ years of technical product or program management experience, working directly with multiple engineering teams.
- 6+ years of experience driving hardware development programs (servers, racks, networking, or storage) through full product lifecycle including design, validation, manufacturing, and fleet deployment
- Experience managing programs across cross-functional teams, building processes, and coordinating release schedules
Preferred qualifications
- Master's degree in Computer Science, Electrical Engineering, or a related field- 5+ years of experience coordinating complex server programs with ODM/JDM partners across multiple geographies, managing design reviews, manufacturing readiness gates, and quality checkpoints
- 5+ years of experience working with GPU/accelerator server platforms, NPI (New Product Introduction), or hardware qualification programs across various phases (EVT, DVT, PVT)
- Experience driving root cause analysis and corrective action processes for hardware reliability issues at fleet scale
- Knowledge of server hardware development lifecycle: electrical/mechanical design, firmware, thermal/power validation, and manufacturing testing.
- Experience driving zero-touch automation or fleet health programs for large-scale infrastructure
- Experience communicating engineering trade-offs and technical risks in business terms to leadership
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.
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Taipei City, TWN
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