NVIDIA

4 weeks ago

Deep Learning Compiler CI/Infrastructure Engineer

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 24 minutes agoStatus: Live

Job Summary

Own and evolve the CI/CD infrastructure powering NVIDIA’s deep learning compiler stacks. Design and operate scalable CI systems for ML workloads across diverse GPU/accelerator environments, ensure reliable correctness and performance signals, and serve as the primary technical contact for CI health, onboarding, and new architecture bring-up. Leverage automation, AI, and agent-based workflows to boost developer efficiency and platform reliability.

Location: Shanghai

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Key Responsibilities

•Build, maintain, and improve CI infrastructure that supports development, verification, and release of NVIDIA’s deep learning compiler stacks across GPU and accelerator environments
•Improve CI reliability and signal quality by reducing flakes, improving reproducibility, strengthening diagnostics, and making correctness and performance failures easier to understand and act on
•Apply automation, AI, and agent-based workflows to reduce manual CI operations, speed up failure triage, and improve developer efficiency
•Build reusable and self-service CI platforms that support multiple products, projects, model suites, hardware targets, and software configurations while partnering closely with compiler, infrastructure, and release teams
•Collaborate with compiler, infrastructure, and release teams to bring up new architectures and onboard projects efficiently

Key Requirements

•BS, MS, or PhD (or equivalent experience) in Computer Science, Computer/Electrical Engineering, Mathematics, or a related field
•5+ years of experience designing, scaling, and operating CI/CD, build/release, or developer infrastructure for complex software systems
•Proven experience building CI platforms end-to-end using GitLab CI, Jenkins, or similar tools, including pipeline orchestration, compute/runner management, artifact and package systems, and observability
•Strong software engineering skills (Python required), with the ability to design, implement, and debug distributed systems end-to-end
•Familiarity with edge devices (SOC, e.g. NVIDIA Tegra) in host-target architecture, ability to debug it and knowledge of its automation nuances

Experience:5+ yearsAIDLGPUCI/CD

Education:Bachelor's

Skills:PythonCI/CDGitLab CIJenkinsDistributed systemsAutomationObservabilityRendering pipelinesEdge devicesLLVM/MLIRTensorRT IR

Languages:English

Tech Stack:PythonGitLab CIJenkinsSlurmKubernetesCloudLLVMMLIRTensorRT IR

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

Deep Learning Compiler CI/Infrastructure Engineer

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 24 minutes agoStatus: Live

Job Summary

Location: Shanghai

Workplace: Onsite

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Key Responsibilities

•Build, maintain, and improve CI infrastructure that supports development, verification, and release of NVIDIA’s deep learning compiler stacks across GPU and accelerator environments
•Improve CI reliability and signal quality by reducing flakes, improving reproducibility, strengthening diagnostics, and making correctness and performance failures easier to understand and act on
•Apply automation, AI, and agent-based workflows to reduce manual CI operations, speed up failure triage, and improve developer efficiency
•Build reusable and self-service CI platforms that support multiple products, projects, model suites, hardware targets, and software configurations while partnering closely with compiler, infrastructure, and release teams
•Collaborate with compiler, infrastructure, and release teams to bring up new architectures and onboard projects efficiently

Key Requirements

•BS, MS, or PhD (or equivalent experience) in Computer Science, Computer/Electrical Engineering, Mathematics, or a related field
•5+ years of experience designing, scaling, and operating CI/CD, build/release, or developer infrastructure for complex software systems
•Proven experience building CI platforms end-to-end using GitLab CI, Jenkins, or similar tools, including pipeline orchestration, compute/runner management, artifact and package systems, and observability
•Strong software engineering skills (Python required), with the ability to design, implement, and debug distributed systems end-to-end
•Familiarity with edge devices (SOC, e.g. NVIDIA Tegra) in host-target architecture, ability to debug it and knowledge of its automation nuances

Experience:5+ yearsAIDLGPUCI/CD

Education:Bachelor's

Skills:PythonCI/CDGitLab CIJenkinsDistributed systemsAutomationObservabilityRendering pipelinesEdge devicesLLVM/MLIRTensorRT IR

Languages:English

Tech Stack:PythonGitLab CIJenkinsSlurmKubernetesCloudLLVMMLIRTensorRT IR

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