NVIDIA

1 day ago

NCX Senior Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 day agoStatus: Live

Job Summary

Collaborate with strategic customers to implement and enhance large-scale AI workloads on NVIDIA’s NCP and Neo Cloud platforms. Build and deploy distributed training and inference solutions, deploy/manage workloads across DGX Cloud and data centers, and tune performance using observability and SLO/SLA monitoring. Act as a technical contact for remote and on-site support, guide partners on NVIDIA platform guidelines, and produce runbooks and implementation documentation.

Location: Santa Clara, Seattle

Workplace: Hybrid

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Mid level

Key Responsibilities

•Build and deploy custom AI solutions on NCP and Neo Cloud, including distributed training, inference optimization, and MLOps pipelines using NVIDIA reference architectures.
•Serve as the main technical contact for strategic NCPs, providing remote and on-site support, troubleshooting complex production issues, and guiding partner engineering on NVIDIA platform guidelines.
•Deploy and manage AI workloads across DGX Cloud, NCP data centers, and major CSP environments using Kubernetes, containers, and GPU scheduling aligned to NCP builds.
•Profile and tune large-scale training and inference workloads; implement observability and SLO/SLA monitoring; work to reduce latency, cost, and operational risk.
•Implement and expand NVIDIA reference architectures on partner platforms, develop integrations for control planes and customer environments, and create implementation guides and runbooks.

Pay and Benefits

Salary: USD 184,000 - 356,500 annually

Equity and Bonus:Equity

Key Requirements

•BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
•8+ years in customer-facing technical roles such as Solutions Engineering, DevOps, Site Reliability, or ML Infrastructure Engineering, ideally for large-scale cloud or service provider environments.
•Strong expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling on multi-tenant or service-provider platforms.
•Demonstrated AI/ML production experience supporting large-scale training and inference (e.g., LLMs, generative models, recommendation systems).
•Solid programming skills in Python/Go, with hands-on experience using PyTorch or TensorFlow for training and serving.

Experience:8+ yearsAIMLMLOpsCloudService providerLLMsGenerative modelsRecommendation systems

Education:Bachelor's in Computer Science; Computer/Electrical Engineering; or related technical field

Skills:CommunicationTechnical presentationCollaborationProblem-solvingTroubleshooting

Tech Stack:NCPNeo CloudDGX CloudKubernetesContainersGPU schedulingLinuxDistributed computingCUDANeMoTritonNIMInfiniBandRoCEMLOpsCI/CDPrometheusGrafanaOpenTelemetryGitOps

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

Loading job details...

Preparing the role view and application actions.

NVIDIA

1 day ago

NCX Senior Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 day agoStatus: Live

Job Summary

Location: Santa Clara, Seattle

Workplace: Hybrid

Employment Type: Full time

Job Function: Solutions Engineering & Sales Engineering

Seniority: Mid level

Key Responsibilities

•Build and deploy custom AI solutions on NCP and Neo Cloud, including distributed training, inference optimization, and MLOps pipelines using NVIDIA reference architectures.
•Serve as the main technical contact for strategic NCPs, providing remote and on-site support, troubleshooting complex production issues, and guiding partner engineering on NVIDIA platform guidelines.
•Deploy and manage AI workloads across DGX Cloud, NCP data centers, and major CSP environments using Kubernetes, containers, and GPU scheduling aligned to NCP builds.
•Profile and tune large-scale training and inference workloads; implement observability and SLO/SLA monitoring; work to reduce latency, cost, and operational risk.
•Implement and expand NVIDIA reference architectures on partner platforms, develop integrations for control planes and customer environments, and create implementation guides and runbooks.

Pay and Benefits

Salary: USD 184,000 - 356,500 annually

Equity and Bonus:Equity

Key Requirements

•BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
•8+ years in customer-facing technical roles such as Solutions Engineering, DevOps, Site Reliability, or ML Infrastructure Engineering, ideally for large-scale cloud or service provider environments.
•Strong expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling on multi-tenant or service-provider platforms.
•Demonstrated AI/ML production experience supporting large-scale training and inference (e.g., LLMs, generative models, recommendation systems).
•Solid programming skills in Python/Go, with hands-on experience using PyTorch or TensorFlow for training and serving.

Experience:8+ yearsAIMLMLOpsCloudService providerLLMsGenerative modelsRecommendation systems

Education:Bachelor's in Computer Science; Computer/Electrical Engineering; or related technical field

Skills:CommunicationTechnical presentationCollaborationProblem-solvingTroubleshooting

Tech Stack:NCPNeo CloudDGX CloudKubernetesContainersGPU schedulingLinuxDistributed computingCUDANeMoTritonNIMInfiniBandRoCEMLOpsCI/CDPrometheusGrafanaOpenTelemetryGitOps

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor