AI and ML Infra Software Engineer, GPU Clusters

NVIDIA

AI Infrastructure Software Engineer collaborating with researchers to enhance AI/ML infrastructure at NVIDIA. Focused on optimizing performance and facilitating innovative AI research on GPU clusters.

Posted 7/23/2026full-timeSanta Clara • California, Washington • 🇺🇸 United StatesMid-LevelSenior💰 $124,000 - $195,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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

Demonstrates expertise in AI/ML infrastructure, including High Performance Computing (HPC) and accelerated computing technologies. Proficient in optimizing large-scale distributed training workloads and collaborating with diverse teams to enhance AI researcher efficiency.

Highest-signal resume keywords

AI/ML InfrastructureHigh Performance Computing (HPC)Distributed Training Workloads OptimizationProgramming Languages (Python, Go, Bash)Cloud Computing Platforms (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 WorkloadsAccelerated ComputingStorage Technologies (Lustre, GPFS, BeeGFS)Scheduling & Orchestration (Slurm, Kubernetes, LSF)High-Speed Networking (Infiniband, RoCE, Amazon EFA)Container Technologies (Docker, Enroot)Parallel Computing FrameworksData ProcessingModel TrainingInference Pipelines

Soft Skills

Excellent CommunicationCollaboration SkillsPassion for Learning

Certifications & Qualifications

MS or PhD in Computer Science or Related Field

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoGoogle Cloud PlatformKubernetesPythonPyTorch

About the role

Key responsibilities & impact

Collaborate closely with our AI and ML research teams to understand their infrastructure needs and obstacles
Monitor and optimize the performance of our infrastructure ensuring high availability, scalability, and efficient resource utilization
Help define and improve important measures of AI researcher efficiency, ensuring that our actions are in line with measurable results
Collaborate with diverse teams, including researchers, data engineers, and DevOps professionals, to build a seamless and coordinated AI/ML infrastructure ecosystem
Stay on top of the latest advancements in AI/ML technologies, frameworks, and effective strategies, and promote their implementation within the company

Requirements

What you’ll need

Recent graduate with a MS, PhD or equivalent experience in Computer Science or related field
Proven experience in AI/ML and HPC workloads and infrastructure
Hands-on experience in using or operating High Performance Computing (HPC) grade infrastructure
In-depth knowledge of accelerated computing (e.g., GPU, custom silicon)
Storage (e.g., Lustre, GPFS, BeeGFS)
Scheduling & orchestration (e.g., Slurm, Kubernetes, LSF)
High-speed networking (e.g., Infiniband, RoCE, Amazon EFA)
Containers technologies (Docker, Enroot)
Expertise in running and optimizing large-scale distributed training workloads using PyTorch (DDP, FSDP), NeMo, or JAX
Deep understanding of AI/ML workflows, encompassing data processing, model training, and inference pipelines
Proficiency in programming & scripting languages such as Python, Go, Bash
Familiarity with cloud computing platforms (e.g., AWS, GCP, Azure)
Experience with parallel computing frameworks and paradigms.
Passion for continual learning and keeping abreast of new technologies and effective approaches in the AI/ML infrastructure field.
Excellent communication and collaboration skills

Benefits

Comp & perks

competitive salaries
comprehensive benefits package