NVIDIA

4 weeks ago

NCX Engineer, AI Accelerator

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Lead design and deployment of AI accelerator workloads for enterprise customers, building scalable NVIDIA reference architectures on NCP and Neo Cloud, optimizing distributed training and inference, and guiding partner teams through GPU-accelerated deployments across DGX Cloud, NCP data centers and major CSP environments.

Location: Shanghai, Beijing, Shenzhen

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build and deploy AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines built on NVIDIA reference architectures.
•Act as the main technical contact for strategic NCPs, provide remote and on-site support, troubleshoot complex production problems, and guide partner engineering teams 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 systems.
•Profile and tune large-scale training and inference workloads on NCP platforms, implement observability and SLO/SLA monitoring, and reduce latency, cost, and operational risk.
•Implement and expand NVIDIA reference architectures on partner platforms, develop integrations with partner control planes and customer environments, ensuring smooth API and data pipeline connectivity.

Key Requirements

•BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
•8+ years of experience in customer facing technical roles such as Solutions Engineering, DevOps, Site Reliability, or ML Infrastructure Engineering, ideally supporting 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 experience supporting large-scale training and inference workloads (e.g., LLMs, generative models, recommendation systems) in production or critically important environments.
•Solid programming skills in Python/Go, with hands-on experience using frameworks such as PyTorch or TensorFlow for training and serving.

Experience:8+ yearsAIMLCloudDistributed systems

Education:Bachelor's

Skills:PythonGoPyTorchTensorFlowKubernetesDockerGPU schedulingLinuxCI/CDObservability

Languages:English

Tech Stack:PythonGoPyTorchTensorFlowKubernetesDockerDGXNCPNeo CloudPrometheusGrafanaOpenTelemetryTerraformAnsible

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

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NVIDIA

4 weeks ago

NCX Engineer, AI Accelerator

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 3 hours agoStatus: Live

Job Summary

Location: Shanghai, Beijing, Shenzhen

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•Build and deploy AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines built on NVIDIA reference architectures.
•Act as the main technical contact for strategic NCPs, provide remote and on-site support, troubleshoot complex production problems, and guide partner engineering teams 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 systems.
•Profile and tune large-scale training and inference workloads on NCP platforms, implement observability and SLO/SLA monitoring, and reduce latency, cost, and operational risk.
•Implement and expand NVIDIA reference architectures on partner platforms, develop integrations with partner control planes and customer environments, ensuring smooth API and data pipeline connectivity.

Key Requirements

•BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
•8+ years of experience in customer facing technical roles such as Solutions Engineering, DevOps, Site Reliability, or ML Infrastructure Engineering, ideally supporting 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 experience supporting large-scale training and inference workloads (e.g., LLMs, generative models, recommendation systems) in production or critically important environments.
•Solid programming skills in Python/Go, with hands-on experience using frameworks such as PyTorch or TensorFlow for training and serving.

Experience:8+ yearsAIMLCloudDistributed systems

Education:Bachelor's

Skills:PythonGoPyTorchTensorFlowKubernetesDockerGPU schedulingLinuxCI/CDObservability

Languages:English

Tech Stack:PythonGoPyTorchTensorFlowKubernetesDockerDGXNCPNeo CloudPrometheusGrafanaOpenTelemetryTerraformAnsible

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