DGX Cloud Performance Engineer - New Grad 2026

Details

NVIDIA DGX™ Cloud is an end-to-end, scalable AI platform for developers, offering scalable capacity built on the latest NVIDIA architecture and co-engineered with the world’s leading cloud service providers (CSPs). We are seeking highly skilled Parallel and Distributed Systems engineers to drive the performance analysis, optimization, and modeling to define the architecture and design of NVIDIA’s DGX Cloud clusters.

The ideal candidate will have a deep understanding of the methodology to conduct end to end performance analysis of critical AI applications running on large scale parallel and distributed systems. Candidates will work closely with cross-functional teams to define DGX Cloud cluster architecture for different CSPs, optimize workloads running on these systems and develop the methodology that will drive the HW-SW co-design cycle to develop world class AI infrastructure at scale and make them more easily consumable by users (via improved scalability, reliability, cleaner abstractions, etc.).

Responsibilities

• Develop benchmarks and end-to-end customer applications running at scale, instrumented for performance measurements, tracking, sampling, to measure and optimize performance of important applications and services.
• Construct carefully designed experiments to analyze, study and develop critical insights into performance bottlenecks and dependencies from an end-to-end perspective.
• Develop ideas to improve end-to-end system performance and usability by driving changes in hardware or software (or both).
• Collaborate with AI researchers, developers, and application service providers to understand internal developer and external customer pain points, requirements, project future needs and share best practices.
• Develop the necessary modeling framework and the TCO (total cost of ownership) analysis to enable efficient exploration and sweep of the architecture and design space.
• Develop the methodology needed to drive the engineering analysis to inform the architecture, design and roadmap of DGX Cloud.

Requirements

• Currently pursuing a Master’s or PhD in Engineering or equivalent experience (preferably Electrical Engineering, Computer Engineering, or Computer Science).
• Experience working with large-scale parallel and distributed accelerator-based systems.
• Experience optimizing performance and AI workloads on large-scale systems.
• Background with performance modeling and benchmarking at scale.
• Background in computer architecture, networking, storage systems, and accelerators.
• Familiarity with popular AI frameworks: PyTorch, TensorFlow, JAX, Megatron-LM, Tensort-LLM, VLLM (among others).
• Experience with AI/ML models and workloads, in particular LLMs, and an understanding of DNNs and their use in emerging AI/ML applications and services.
• Proficiency in Python and C/C++.
• Expertise with at least one public CSP infrastructure (GCP, AWS, Azure, OCI, …).

Ways to stand out

• PhD in relevant areas.
• Very high intellectual curiosity; confidence to dig in as needed; not afraid of confronting complexity; able to pick up new areas quickly.
• Proficiency in CUDA and XLA.
• Excellent interpersonal skills.

Compensation & Benefits

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 148,000 USD - 235,750 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits: https://www.nvidia.com/en-us/benefits/.

Applications for this job will be accepted at least until December 19, 2025.

Equal Opportunity

NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer. 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.