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职位NVIDIA

Software Engineering Intern, Automation Infra - 2026

NVIDIA

Software Engineering Intern, Automation Infra - 2026

NVIDIA

China, Shanghai

·

On-site

·

Internship

·

3d ago

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

Become a Software Engineer Intern at NVIDIA and join the Perf Lab team! This exceptional opportunity enables you to work with leading experts and support groundbreaking projects in an inclusive and varied environment.

What you'll be doing:

  • Build and maintain AI agent skills, MCP tools, and agentic workflows to automate Perf Lab operations and boost team efficiency.

  • Develop and deploy cloud-native services using Kubernetes, Helm, Argo CD, and Terraform on internal infrastructure.

  • Build and implement data automation pipelines and infrastructure for performance test result collection, transformation, and visualization.

  • Build interactive dashboards (Streamlit, HTML) to present benchmark insights to internal and external customers.

  • Contribute to open-source workflow orchestration tools for GPU cluster workloads.

  • Work with NVIDIA's global experts to tackle intriguing challenges and drive impactful project developments.

What we need to see:

  • Pursuing a degree in Computer Science, Electrical Engineering, or a related field.

  • Strong Python skills with experience in building CLI tools, REST APIs, or automation scripts in Unix and Windows environments.

  • Hands-on experience building AI-powered applications such as AI agents, RAG pipelines, or MCP tools.

  • Familiarity with cloud-native technologies: Kubernetes, Docker, CI/CD pipelines (GitLab CI preferred).

  • Good understanding of ML/DL workloads (CNNs, Transformers, LLMs).

  • Proficient in applying leading AI coding tools (e.g., Claude Code, Cursor, Codex) to accelerate daily work.

  • Strong collaborative and interpersonal skills, effective influence within a dynamic environment.

  • Excellence in written and oral English.

Ways to stand out from the crowd:

  • Prior experience with agentic LLM frameworks like Lang Chain, Lang Graph, or Llama Index.

  • Contributions to open-source projects in DevOps, infrastructure, or AI tooling.

  • Proven track record of innovation and problem-solving, showcased through hackathons, published papers, competitions, or innovative projects.

  • Experience writing technical blogs, giving tech talks, or creating demo videos to promote project outcomes.

Successfully implement your skills and join us in crafting the future of AI and computing!

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/

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关于NVIDIA

NVIDIA

NVIDIA

Public

A computing platform company operating at the intersection of graphics, HPC, and AI.

10,001+

员工数

Santa Clara

总部位置

$4.57T

企业估值

评价

4.1

10条评价

工作生活平衡

3.5

薪酬

4.2

企业文化

4.3

职业发展

4.5

管理层

4.0

75%

推荐给朋友

优点

Great culture and supportive environment

Smart colleagues and excellent people

Cutting-edge technology and learning opportunities

缺点

Team-dependent experience and outcomes

Work-life balance issues with long hours

Politics and influence over competence

薪资范围

73个数据点

Junior/L3

Mid/L4

Junior/L3 · Analyst

7份报告

$170,275

年薪总额

基本工资

$130,981

股票

-

奖金

-

$155,480

$234,166

面试经验

7次面试

难度

3.1

/ 5

体验

正面 0%

中性 86%

负面 14%

面试流程

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Interview

5

System Design Interview

6

Team Review

常见问题

Coding/Algorithm

System Design

Technical Knowledge

Behavioral/STAR