Principal AI and ML Infra Software Engineer, GPU Clusters

NVIDIA

. Engage with AI and ML research teams to identify infrastructure requirements and barriers and translate insights into actionable improvements .

Posted 10/14/2026full-timeUnited StatesLead💰 $272,000 - $431,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive expertise in AI/ML and HPC systems, with a strong focus on optimizing infrastructure for researcher efficiency and high-performance computing. Proficient in Python, Go, and Bash, with hands-on experience in managing distributed training operations and cloud computing platforms.

Highest-signal resume keywords

AI/ML ExpertiseHigh Performance Computing (HPC)Distributed Training OperationsPython ProgrammingCloud Computing (AWS, GCP, Azure)

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI/ML WorkflowsAccelerated ComputingData ProcessingModel TrainingInference PipelinesParallel Computing FrameworksContainer TechnologiesHigh-Speed NetworkingScheduling and OrchestrationPyTorch

Soft Skills

Excellent CommunicationCollaboration Skills

Tools & Technologies

AWSGCPAzureNeMoJAX

Industry Keywords

Researcher EfficiencyInfrastructure PerformanceResource UtilizationAI/ML Infrastructure Ecosystem

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonPyTorchGo

About the role

Key responsibilities & impact

Engage with AI and ML research teams to identify infrastructure requirements and barriers and translate insights into actionable improvements
Identify researcher-efficiency bottlenecks and lead initiatives to improve efficiency systematically
Drive direction and long-term roadmaps for researcher-efficiency initiatives
Monitor and optimize infrastructure performance for high availability, scalability, and efficient resource utilization
Define and improve measures of AI researcher efficiency
Collaborate with researchers, data engineers, and DevOps professionals to develop an AI/ML infrastructure ecosystem
Track developments in AI/ML technologies, frameworks, and successful strategies and advocate for their integration

Requirements

What you’ll need

BS or similar background in Computer Science or related area (or equivalent experience)
15+ years of demonstrated expertise in AI/ML and HPC tasks and systems
Hands-on experience using or operating High Performance Computing (HPC) grade infrastructure
In-depth knowledge of accelerated computing, storage, scheduling and orchestration, high-speed networking, and container technologies
Experience supervising and improving substantial distributed training operations using PyTorch (DDP, FSDP), NeMo, or JAX
In-depth understanding of AI/ML workflows, including data processing, model training, and inference pipelines
Proficiency in Python, Go, and Bash
Familiarity with cloud computing platforms such as AWS, GCP, and Azure
Experience with parallel computing frameworks and paradigms
Dedication to ongoing learning and staying updated on new technologies and innovative methods in AI/ML infrastructure
Excellent communication and collaboration skills
Equivalent experience may substitute for the stated BS background

Benefits

Comp & perks

Competitive salaries
Comprehensive benefits package
Equity