
Pioneering accelerated computing and AI
Technical Marketing Engineer - DGX Cloud
必备技能
Kubernetes
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.
The NVIDIA Cloud Accelerator team develops innovative full stack AI solutions for data centers. We seek a Technical Marketing Engineer passionate about AI Infrastructure, Cloud, and Data Center Management software. You will help our cloud and ecosystem partners deploy and use our technology. You will also demonstrate how the different components integrate across the full stack. Acting as a bridge between complex data center deployments and cloud-native AI software, you will assist users in configuring physical systems, networking, and multi-tenant software to support AI solutions and users. If you're passionate about building scalable, efficient systems to power the latest in innovative AI, we invite you to join our team. At NVIDIA, you’ll be immersed in a diverse, welcoming environment where everyone feels encouraged to do their best work. Come join the team and see how you can make a lasting impact on the world.
What you’ll be doing:
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Illustrate methods for deploying and managing infrastructure software: Build automation, demos, and reference examples showing how to deploy and manage NVIDIA’s infrastructure software components.
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Build and drive integrations across the stack: Collaborate with Technical Marketing Engineering, Product, Engineering, and Field teams to demonstrate how different layers of data center hardware, infrastructure management software, AI platforms, and AI use cases work together.
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Enable the field with compelling technical assets: Publish content like reference architectures, deployment guides, and code examples to help field teams and partners integrate our technologies. This will involve customer-facing engagements alongside our Solution Architects.
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Engage and support the developer community: Advocate for our technology within the cloud-native ecosystem, encouraging support and adoption of NVIDIA’s infrastructure software.
What we need to see:
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8+ years data center and infrastructure expertise: Strong understanding of configuring and deploying data center systems, including compute nodes, storage, and networking.
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Experience with cloud-native deployments: Familiarity with managing containerized deployments and working with Kubernetes clusters.
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Experience with infrastructure-as-code: Experience in developing automation for managing and deploying infrastructure software and cloud-native technologies.
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Cloud computing and security: Background in cloud AI/ML platforms, cloud security concepts, and multi-tenant architecture.
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Cross-functional collaboration: Ability to work alongside multiple teams and customers, providing valuable input and feedback while steering the progress of NVIDIA’s technologies.
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Technical prowess: Ability to delve into and contribute to engineering developments, defend technical opinions, and make informed prioritized decisions, acting independently with strong problem-solving skills.
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BS or MS in engineering, computer science, or another technical field (or equivalent experience).
Ways to stand out from the crowd:
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Experience with more than one cloud service provider.
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Familiarity working with NVIDIA DPUs and DOCA and/or switch operating systems like Cumulus Linux and MLNX-OS.
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Experience with managing security of the tenant and/or physical/virtual environments and understanding of secure key management and certificate lifecycle operations (PKI concepts, OCSP, CRLs, enrollment protocols, key rotation, revocation, etc).
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Familiarity with NVIDIA technologies and products including Base Command Manager, DGX Cloud, Run:ai, GPU Operator, Network Operator, etc.
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Experience with Slurm and/or Kubernetes-based MLOps platforms.
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/
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 160,000 USD - 253,000 USD for Level 4, and 200,000 USD - 322,000 USD for Level 5.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until March 10, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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关于NVIDIA

NVIDIA
PublicA 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
10条评价
4.4
10条评价
工作生活平衡
2.8
薪酬
4.5
企业文化
4.2
职业发展
4.3
管理层
3.8
78%
推荐率
优点
Cutting-edge technology and innovation
Excellent compensation and benefits
Great team culture and collaboration
缺点
High pressure and expectations
Poor work-life balance and long hours
Fast-paced environment leading to burnout
薪资范围
79个数据点
Junior/L3
Mid/L4
Senior/L5
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
面试评价
5条评价
难度
3.0
/ 5
面试流程
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Past Experience


