AI Platform Engineer Jobs in North Carolina
AI Platform Engineer jobs in North Carolina are in strong demand, concentrated in the technology, financial services, and life sciences sectors, with openings at every level from entry-level ML infrastructure roles through principal and staff engineer positions. Raleigh-Durham, Charlotte, and the Research Triangle Park corridor account for the largest share of hiring, anchored by employers such as Red Hat, Lenovo, and Bank of America. The most sought-after specialties in North Carolina include MLOps pipeline development, large language model deployment infrastructure, and cloud-native AI tooling on AWS and Azure. Find a role that fits below and apply directly.
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General Information
- United States of America - North Carolina - Morrisville
Why Work at Lenovo
Description and Requirements
Position Overview
Lenovo Infrastructure Solutions Group (ISG) is seeking an experienced GPU Platform SME to help evaluate, develop, and validate next-generation GPU-based AI infrastructure platforms.
In this role, you will work closely with GPU architects, platform engineering teams, and GPU suppliers to define and execute integrated validation strategies across node-level (L10), rack-level (L11), and cluster-level (L12) environments. You will be responsible for studying emerging GPU platform architectures, ensuring validation readiness, executing complex validation activities, and driving technical issues from discovery through root cause analysis and resolution.
The ideal candidate combines deep expertise in server platform architecture, GPU validation, AI infrastructure, and high-speed networking technologies with the ability to lead failure analysis efforts and collaborate across global engineering organizations and supplier partners. This role requires end-to-end ownership of validation findings, including debug experiment design, issue triage, root cause investigation, and validation closure for next-generation accelerated computing platforms.
Job Responsibilities
GPU Platform Architecture & Validation Strategy
- Study next-generation GPU platform architectures, reference designs, and supplier documentation to understand platform capabilities, design changes, validation requirements, and deployment considerations.
- Perform comparative analysis between incoming and outgoing platform generations, identifying validation impacts, readiness gaps, risks, and infrastructure requirements.
- Review supplier validation requirements (such as NVIDIA Platform Validation Programs) and Lenovo validation strategies to determine execution prerequisites, validation coverage plans, and test readiness requirements.
- Work closely with GPU architects and engineering stakeholders to translate platform requirements into executable validation strategies.
- Assess and document requirements for validation coverage, infrastructure readiness, test environments, and platform qualification activities.
Validation Readiness & Infrastructure Planning
- Define and coordinate validation environment requirements, including lab infrastructure, test equipment, fixtures, software tools, utilities, licenses, and system configurations required for successful execution.
- Review Lenovo validation plans and test requirements across multiple engineering disciplines, including Platform Architecture, Electrical Engineering, Enterprise Architecture, Thermal Engineering, and Reliability Engineering.
- Partner with engineering teams to establish validation readiness plans for new platform introductions and technology transitions.
- Support validation framework deployment, test environment setup, and operational readiness activities across server, rack, and cluster environments.
System Bring-Up & Process Development
- Collaborate with GPU architects and engineering teams to develop system bring-up processes, work instructions, validation procedures, and standard operating procedures (SOPs).
- Establish test methodologies, execution processes, and validation best practices to ensure consistent and repeatable test results.
- Support platform integration activities and validation environment preparation throughout the product development lifecycle.
- Drive process improvements that enhance validation efficiency, coverage, and product quality.
Validation Execution & Test Ownership
- Lead validation activities for next-generation GPU-enabled server platforms at the node (L10), rack (L11), and cluster (L12) levels.
- Execute system integration, functional, stress, performance, scalability, and reliability testing for advanced AI and accelerated computing platforms.
- Develop and execute validation plans covering hardware, firmware, operating systems, networking, storage, management controllers, device drivers, software applications, and GPU subsystems.
- Validate AI infrastructure technologies, including GPU interconnects, high-speed networking fabrics, rack integration, power delivery systems, cooling solutions, and cluster deployments.
- Ensure validation milestones, quality objectives, and execution schedules are achieved.
Failure Analysis, Debug & Root Cause Investigation
- Own technical issues throughout the complete validation lifecycle, including issue discovery, triage, experiment design, failure reproduction, root cause analysis, corrective action identification, and validation closure.
- Investigate complex hardware, firmware, software, and infrastructure issues using log analysis, diagnostics, system telemetry, and structured debugging techniques.
- Perform technical experiments and data-driven analysis to isolate failures and determine root cause.
- Drive issue resolution with cross-functional engineering teams and ensure corrective actions are properly validated.
- Provide technical leadership during failure analysis reviews and validation risk assessments.
Cross-Functional & Supplier Collaboration
- Collaborate closely with Platform Architecture, Electrical Engineering, Thermal Engineering, Reliability Engineering, Software Engineering, Manufacturing Engineering, and Product Development teams.
- Partner directly with GPU suppliers and engineering counterparts on product-specific issues, validation findings, failure analysis, corrective actions, and technical escalations.
- Work effectively with global engineering organizations, ODM/OEM partners, hyperscale customers, and technology suppliers to achieve validation and product quality objectives.
- Support technical reviews, milestone readiness assessments, product release activities, and validation signoff processes.
- Drive technical discussions and provide validation expertise across global cross-functional teams.
Job Requirements
Required Qualifications
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, Computer and Information Science, or a related technical discipline.
- 6+ years of experience in server platform development, system validation, system integration, hardware validation, AI infrastructure, accelerated computing platforms, or related engineering disciplines.
- Strong knowledge of enterprise server architecture and system integration, including:
- BIOS/UEFI
- Baseboard Management Controllers (BMC)
- Operating systems
- CPU and memory subsystems
- Storage technologies
- Networking technologies
- PCIe devices
- Firmware
- Device drivers
- Software applications
- Strong understanding of GPU interconnect and high-speed networking technologies, including:
- PCIe
- Ethernet
- InfiniBand
- NVLink
- NVSwitch
- Related GPU fabric technologies
- Hands-on experience performing:
- System integration testing
- Functional validation
- Stress testing
- Performance testing
- Failure analysis
- Troubleshooting and root cause investigations
- Working knowledge of Linux administration, command-line diagnostics, log analysis, and server troubleshooting methodologies and tools.
- Demonstrated ability to independently drive complex technical issues from identification through root cause determination and resolution.
- Strong written and verbal communication skills with the ability to collaborate effectively across global cross-functional engineering teams.
Preferred Qualifications
- Hands-on experience with one or more advanced AI infrastructure platforms, including:
- NVIDIA HGX
- NVIDIA DGX
- GB200
- GB300
- NVL72
- Next-generation GPU architectures
- Experience with rack-scale AI systems, GPU clusters, and large-scale accelerated computing environments utilizing:
- NVLink
- NVSwitch
- InfiniBand fabrics
- High-speed Ethernet fabrics
- Experience supporting hyperscale, cloud, AI infrastructure, OEM, ODM, server development, manufacturing validation, or large-scale data center deployments.
- Familiarity with NVIDIA validation and diagnostic tools, including:
- DCGM
- NVDiag
- NVQual
- Experience with NVIDIA CUDA, AMD ROCm, or other GPU software ecosystems.
- Experience developing validation automation, diagnostic solutions, or validation frameworks using Python, Shell scripting, or similar programming languages.
- Familiarity with liquid cooling infrastructure, power distribution systems, rack integration technologies, and data center deployment considerations.
- Experience collaborating directly with GPU suppliers, hyperscale customers, ODM/OEM partners, or global engineering organizations.
- Mandarin language capability is highly preferred due to collaboration with global engineering teams and supplier partners.
- United States of America - North Carolina - Morrisville
- United States of America
- United States of America - North Carolina
- United States of America - North Carolina - Morrisville
General Information
- United States of America - North Carolina - Morrisville
Why Work at Lenovo
Description and Requirements
Position Overview
Lenovo Infrastructure Solutions Group (ISG) is seeking an experienced GPU Platform SME to help evaluate, develop, and validate next-generation GPU-based AI infrastructure platforms.
In this role, you will work closely with GPU architects, platform engineering teams, and GPU suppliers to define and execute integrated validation strategies across node-level (L10), rack-level (L11), and cluster-level (L12) environments. You will be responsible for studying emerging GPU platform architectures, ensuring validation readiness, executing complex validation activities, and driving technical issues from discovery through root cause analysis and resolution.
The ideal candidate combines deep expertise in server platform architecture, GPU validation, AI infrastructure, and high-speed networking technologies with the ability to lead failure analysis efforts and collaborate across global engineering organizations and supplier partners. This role requires end-to-end ownership of validation findings, including debug experiment design, issue triage, root cause investigation, and validation closure for next-generation accelerated computing platforms.
Job Responsibilities
GPU Platform Architecture & Validation Strategy
- Study next-generation GPU platform architectures, reference designs, and supplier documentation to understand platform capabilities, design changes, validation requirements, and deployment considerations.
- Perform comparative analysis between incoming and outgoing platform generations, identifying validation impacts, readiness gaps, risks, and infrastructure requirements.
- Review supplier validation requirements (such as NVIDIA Platform Validation Programs) and Lenovo validation strategies to determine execution prerequisites, validation coverage plans, and test readiness requirements.
- Work closely with GPU architects and engineering stakeholders to translate platform requirements into executable validation strategies.
- Assess and document requirements for validation coverage, infrastructure readiness, test environments, and platform qualification activities.
Validation Readiness & Infrastructure Planning
- Define and coordinate validation environment requirements, including lab infrastructure, test equipment, fixtures, software tools, utilities, licenses, and system configurations required for successful execution.
- Review Lenovo validation plans and test requirements across multiple engineering disciplines, including Platform Architecture, Electrical Engineering, Enterprise Architecture, Thermal Engineering, and Reliability Engineering.
- Partner with engineering teams to establish validation readiness plans for new platform introductions and technology transitions.
- Support validation framework deployment, test environment setup, and operational readiness activities across server, rack, and cluster environments.
System Bring-Up & Process Development
- Collaborate with GPU architects and engineering teams to develop system bring-up processes, work instructions, validation procedures, and standard operating procedures (SOPs).
- Establish test methodologies, execution processes, and validation best practices to ensure consistent and repeatable test results.
- Support platform integration activities and validation environment preparation throughout the product development lifecycle.
- Drive process improvements that enhance validation efficiency, coverage, and product quality.
Validation Execution & Test Ownership
- Lead validation activities for next-generation GPU-enabled server platforms at the node (L10), rack (L11), and cluster (L12) levels.
- Execute system integration, functional, stress, performance, scalability, and reliability testing for advanced AI and accelerated computing platforms.
- Develop and execute validation plans covering hardware, firmware, operating systems, networking, storage, management controllers, device drivers, software applications, and GPU subsystems.
- Validate AI infrastructure technologies, including GPU interconnects, high-speed networking fabrics, rack integration, power delivery systems, cooling solutions, and cluster deployments.
- Ensure validation milestones, quality objectives, and execution schedules are achieved.
Failure Analysis, Debug & Root Cause Investigation
- Own technical issues throughout the complete validation lifecycle, including issue discovery, triage, experiment design, failure reproduction, root cause analysis, corrective action identification, and validation closure.
- Investigate complex hardware, firmware, software, and infrastructure issues using log analysis, diagnostics, system telemetry, and structured debugging techniques.
- Perform technical experiments and data-driven analysis to isolate failures and determine root cause.
- Drive issue resolution with cross-functional engineering teams and ensure corrective actions are properly validated.
- Provide technical leadership during failure analysis reviews and validation risk assessments.
Cross-Functional & Supplier Collaboration
- Collaborate closely with Platform Architecture, Electrical Engineering, Thermal Engineering, Reliability Engineering, Software Engineering, Manufacturing Engineering, and Product Development teams.
- Partner directly with GPU suppliers and engineering counterparts on product-specific issues, validation findings, failure analysis, corrective actions, and technical escalations.
- Work effectively with global engineering organizations, ODM/OEM partners, hyperscale customers, and technology suppliers to achieve validation and product quality objectives.
- Support technical reviews, milestone readiness assessments, product release activities, and validation signoff processes.
- Drive technical discussions and provide validation expertise across global cross-functional teams.
Job Requirements
Required Qualifications
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, Computer and Information Science, or a related technical discipline.
- 6+ years of experience in server platform development, system validation, system integration, hardware validation, AI infrastructure, accelerated computing platforms, or related engineering disciplines.
- Strong knowledge of enterprise server architecture and system integration, including:
- BIOS/UEFI
- Baseboard Management Controllers (BMC)
- Operating systems
- CPU and memory subsystems
- Storage technologies
- Networking technologies
- PCIe devices
- Firmware
- Device drivers
- Software applications
- Strong understanding of GPU interconnect and high-speed networking technologies, including:
- PCIe
- Ethernet
- InfiniBand
- NVLink
- NVSwitch
- Related GPU fabric technologies
- Hands-on experience performing:
- System integration testing
- Functional validation
- Stress testing
- Performance testing
- Failure analysis
- Troubleshooting and root cause investigations
- Working knowledge of Linux administration, command-line diagnostics, log analysis, and server troubleshooting methodologies and tools.
- Demonstrated ability to independently drive complex technical issues from identification through root cause determination and resolution.
- Strong written and verbal communication skills with the ability to collaborate effectively across global cross-functional engineering teams.
Preferred Qualifications
- Hands-on experience with one or more advanced AI infrastructure platforms, including:
- NVIDIA HGX
- NVIDIA DGX
- GB200
- GB300
- NVL72
- Next-generation GPU architectures
- Experience with rack-scale AI systems, GPU clusters, and large-scale accelerated computing environments utilizing:
- NVLink
- NVSwitch
- InfiniBand fabrics
- High-speed Ethernet fabrics
- Experience supporting hyperscale, cloud, AI infrastructure, OEM, ODM, server development, manufacturing validation, or large-scale data center deployments.
- Familiarity with NVIDIA validation and diagnostic tools, including:
- DCGM
- NVDiag
- NVQual
- Experience with NVIDIA CUDA, AMD ROCm, or other GPU software ecosystems.
- Experience developing validation automation, diagnostic solutions, or validation frameworks using Python, Shell scripting, or similar programming languages.
- Familiarity with liquid cooling infrastructure, power distribution systems, rack integration technologies, and data center deployment considerations.
- Experience collaborating directly with GPU suppliers, hyperscale customers, ODM/OEM partners, or global engineering organizations.
- Mandarin language capability is highly preferred due to collaboration with global engineering teams and supplier partners.
- United States of America - North Carolina - Morrisville
- United States of America
- United States of America - North Carolina
- United States of America - North Carolina - Morrisville
See All 31 AI Platform Engineer Jobs in North Carolina
Find roles in North Carolina that match your experience and apply in just a few clicks.
Find AI Platform Engineer JobsAI Platform Engineer Jobs by City in North Carolina
Where North Carolina roles are concentrated, by current openings.
AI Platform Engineer Job Market in North Carolina
A snapshot from current North Carolina openings, updated as new roles post.
Who's Hiring
Top Industries Hiring
- Technology & Software
- Banking & Financial Services
- Consulting & Professional Services
What North Carolina Employers Look For
The qualifications that appear most often in AI platform engineer jobs across North Carolina.
- Bachelor's or master's degree in computer science, data engineering, or a related technical field
- Hands-on experience building and maintaining ML pipelines and model deployment infrastructure
- Proficiency with cloud platforms such as AWS SageMaker, Azure Machine Learning, or Google Vertex AI
- Strong programming skills in Python with experience in orchestration tools like Kubeflow or Apache Airflow
- Familiarity with containerization and Kubernetes for scalable AI workload management
- Experience integrating CI/CD practices into machine learning workflows using tools like MLflow or DVC
AI Platform Engineer Jobs in North Carolina: Frequently Asked Questions
How do you become a ai platform engineer in North Carolina?
AI platform engineer roles in North Carolina do not require a state-issued license or credential. Most employers in the Research Triangle and Charlotte markets hire candidates with a bachelor's degree in computer science, software engineering, or a related field, combined with demonstrated experience in cloud infrastructure and ML tooling. Building a portfolio of deployed ML pipelines, contributing to open-source MLOps projects, and earning cloud certifications from AWS or Azure strengthens candidacy considerably with North Carolina employers.
How much do AI platform engineers make in North Carolina?
AI platform engineers in North Carolina earn a median of about $104,850 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $39,190 for the lowest 10% to over $175,660 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire ai platform engineers in North Carolina?
Employers hiring ai platform engineers in North Carolina right now include Bank of America, Lenovo, and Sysdig, based on current listings on Migrate Mate as of November 2026. North Carolina's concentration of technology companies in the Research Triangle Park area and major financial institutions headquartered in Charlotte makes the state one of the more active markets for this role in the Southeast.
Which North Carolina cities have the most ai platform engineer jobs?
Charlotte, Raleigh, and Morrisville have the most ai platform engineer openings in North Carolina. Raleigh and Durham dominate because of the dense cluster of technology companies, research universities, and life sciences firms in and around Research Triangle Park, while Charlotte's large financial services and fintech sector drives consistent demand for AI infrastructure talent in that metro.
Are there remote ai platform engineer jobs in North Carolina?
Yes, and more than most fields. AI platform engineering is largely cloud-based and tooling-focused work, making it well-suited to remote arrangements. About 69% of ai platform engineer openings tied to North Carolina are remote or hybrid as of November 2026, reflecting employer flexibility in this discipline. Infrastructure design, pipeline development, and model monitoring tasks are the functions most commonly performed fully remote.
How can I get hired as a ai platform engineer in North Carolina with little or no experience?
The most realistic entry path is joining a North Carolina technology company or large enterprise as a data or software engineer and transitioning into AI platform work once you have exposure to ML workflows. Companies in the Research Triangle Park ecosystem and Charlotte's fintech sector regularly hire new graduates into infrastructure or cloud engineering associate roles that overlap with AI platform responsibilities. Building a portfolio of end-to-end MLOps projects using open-source tools and earning an AWS or Azure cloud practitioner certification gives candidates a concrete edge when applying to these teams.
Where can I find and apply to ai platform engineer jobs in North Carolina?
You can find and apply to ai platform engineer jobs in North Carolina on Migrate Mate, which lists current openings across the state. Search the roles available, find the positions that fit your background and location preference, and apply directly to the ones that match.
See All 31 AI Platform Engineer Jobs in North Carolina
Find roles in North Carolina that match your experience and apply in just a few clicks.
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