NVIDIA is seeking a Compute Kernel Performance Architect who can develop, profile, and analyze CUDA workloads with a strong focus on GPU power behavior. In this role, you will create specialized workloads that exercise the GPU’s compute, memory, and I/O subsystems under demanding operating conditions. You will work closely with GPU architects, power architects, silicon validation engineers, and software teams to characterize workload behavior and influence the power architecture of future NVIDIA products. This position sits at the intersection of GPU architecture, high-performance software, and silicon characterization.
What You'll Be Doing:
Design and develop CUDA kernels and infrastructure that exercise worst-case power behavior across GPU compute, memory, and I/O subsystems.
Profile workloads to understand the relationship between kernel behavior, hardware utilization, performance, and power consumption.
Build workloads that generate controlled steady-state and transient power conditions across multiple GPU architectures.
Partner with GPU architects and silicon teams to identify functional units and workload patterns that require additional characterization.
Support power-stress methodology from pre-silicon modeling and simulation through post-silicon bring-up and validation.
What We Need to See:
MS or PhD or equivalent experience in Computer Science, Electrical Engineering, Computer Engineering, or a related field—or equivalent practical experience.
5+ years of experience in CUDA programming, GPU kernel development, high-performance computing, or performance architecture.
Hands-on experience developing and optimizing GPU kernels, including work at the PTX or assembly level.
Experience with GPU performance-analysis tools such as Nsight Compute, Nsight Systems, nvprof, or equivalent tools.
Strong understanding of GPU build principles, including streaming multiprocessors, execution pipelines, memory hierarchy, synchronization, occupancy, and power states.
Excellent analytical, debugging, and communication skills.
Ability to work effectively across GPU architecture, software, silicon validation, and hardware engineering teams.
Ways to Stand Out from the Crowd:
Experience crafting GPU power-stress microbenchmarks or test-to-failure workloads.
Familiarity with Power Delivery Network concepts, including package and board-level behavior, impedance, inductance, decoupling, resonance, voltage droop, and overshoot.
Understanding of di/dt and how changes in current over time can compose voltage transients.
Experience with DVFS, AVFS, clock management, power states, or hardware noise-mitigation mechanisms.
Knowledge of how software workload patterns can interact with system-level power-delivery behavior.
Our team works at the core of NVIDIA’s GPU performance and power stack. We collaborate closely with Compute Architecture, Power Architecture, Silicon Solutions, circuit-design teams, and deep-learning software teams. The workloads, tools, and analysis produced by this team help validate current products and influence the build of upcoming NVIDIA GPUs.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.You will also be eligible for equity and benefits.
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.Skills Required
- MS or PhD in Computer Science, Electrical Engineering, or Computer Engineering
- 5+ years of experience in GPU kernel development or CUDA programming
- Strong CUDA and C++ programming skills
- Experience with GPU performance profiling tools
- Solid understanding of GPU architecture and power delivery networks
- Strong programming skills in Python
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
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