Top 2008 Benchmarking engineer Jobs

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Systems Software Engineer, Accelerated Kubernetes Performance and Scale - New College Grad 2026

NVIDIA -
Seattle, WA

Apply Now

Job Details

Full-time
$108,000 - $195,500 a year
3 days ago

Qualifications

Containerization systems
Data storage
System performance optimization
Scalable systems
Computer networking
Scalability
Benchmarking
Software architecture

Full Job Description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years, driven by great technology and amazing people. We’re now tapping into the unlimited potential of AI to define the next era of computing, where our GPUs power 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 work in a diverse, supportive environment where people are encouraged to do their best work and grow their careers. We offer a preference for hybrid work while remaining open to remote arrangements, giving you flexibility in how you do your best work.

Come join the team and see how you can make a lasting impact on the world. The DGX Cloud organization at NVIDIA brings together cutting‑edge hardware and software innovation to deliver industry‑leading accelerated computing for the world’s most ambitious AI workloads. We are a group of forward‑thinking engineers tackling some of the globe’s toughest challenges, pushing progress, and positively affecting millions of lives.

What you'll be doing:

Work on end‑to‑end performance and scalability analysis across the Kubernetes‑based accelerated runtime stack (control and data planes), including NVIDIA components such as GPU Operator, Network Operator, node-feature-discovery, topograph, dra-driver-nvidia-gpu, and nvsentinel, tracking issues from orchestration down to the metal.
Design and contribute upstream architectural changes to the Kubernetes control plane and related projects to enable reliable operation at hyperscale cluster sizes, doing in the open what today’s hyperscalers typically do privately.
Improve container startup and cold‑start latency to enable smooth, low‑latency inference scaling on Kubernetes across thousands of GPU nodes, ensuring the AI runtime stack scales without creating API server pressure or operational fragility.
Assess, improve, and contribute to open‑source projects that make Kubernetes an outstanding platform for AI workloads (for example, Grove and gateway-api‑inference‑extension), composing their architectures with scalability, resilience, and multi‑node training/inference in mind.
Advance scalability and performance of confidential containers (CoCo) on Kubernetes so encrypted inference workloads meet stringent efficiency and latency requirements in production.
Use DSX and related large‑scale simulation infrastructure to model full AI‑factory deployments and validate scalability across thousands of simulated GPUs, catching failures that emerge only at scale before hardware arrives.
Collaborate with AI researchers, developers, customers, and upstream communities to design automated, at‑scale workload tests (including replay of production agent traces), build monitoring/analysis tooling, and integrate continuous performance and scale testing into modern CI/CD workflows.
Document methods and results clearly and present findings internally and at industry events (for example, KubeCon, GTC), while actively engaging with upstream groups (Kubernetes SIG Scalability, CNCF, and NVIDIA OSS communities) to influence and validate AI workload performance and scalability directions.

What we need to see:

Recent graduate of a Bachelor’s, Master’s, or PhD degree in Engineering or equivalent experience, ideally in Electrical, Computer Engineering, or Computer Science
Experience in computer architecture, networking, storage systems, and accelerator‑based platforms
Expertise in Kubernetes and familiarity with the broader CNCF ecosystem
Experience with large‑scale, parallel, distributed accelerator systems and performance optimization of AI workloads
Experience with performance modeling and benchmarking for large‑scale systems
Proficiency in Golang and/or Python
Familiarity with the NVIDIA software stack across training and inference
Experience with at least one major public cloud provider (for example, AWS, Azure, GCP, or OCI)

Ways to stand out from the crowd:

Strong operational experience with any one of the Kubernetes distributions
Prior experience scaling Kubernetes clusters to ultra-large node and object counts
Demonstrated history of working in the open-source community
Excellent communication and interpersonal abilities

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 108,000 USD - 178,250 USD for Level 1, and 124,000 USD - 195,500 USD for Level 2.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 21, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive 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.

Apply Now

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benchmarking engineer jobs

Systems Software Engineer, Accelerated Kubernetes Performance and Scale - New College Grad 2026

Senior Systems Software Engineer, Accelerated Kubernetes Performance and Scale - DGX Cloud

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Cost Analyst Engineer

R&D Design Engineer (On-Site)

Principal Systems Engineer

Principal Systems Engineer

Engineering Presales

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