NVIDIA has continuously reinvented itself. Our invention of the GPU sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. Today, research in artificial intelligence is booming worldwide, which calls for highly scalable and massively parallel computation horsepower that NVIDIA GPUs excel.
NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can address, and that matter to the world. This is our life’s work , to amplify human creativity and intelligence. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join our diverse team and see how you can make a lasting impact on the world. As part of this team, you would be working on projects that will help make our next generation visual computing, automotive, GPU, HPC systems better. You will get to work on high performance CPU and Memory sub-systems, Next-Gen GPUs , NOC based Interconnect Fabric etc. Make the choice to join us today.
What you'll be doing:
System level performance analysis/ bottleneck analysis of complex, high performance GPUs and System-on-Chips (SoCs).
Work on hardware models of different levels of abstraction, including performance models, RTL test benches ,emulators and silicon to analyze performance and find performance bottlenecks in the system.
Understand key performance use-cases of the product. Develop workloads and test suits targeting graphics, machine learning, automotive, video, compute vision applications running on these products.
Work closely with the architecture and design teams to explore architecture trade-offs related to system performance, area, and power consumption.
Develop required infrastructure including performance models, testbench components, performance analysis and visualization tools.
Drive methodologies for improving turnaround time, finding representative data-sets and enabling performance analysis early in the product development cycle.
What we need to see:
BE/BTech, or MS/MTech in relevant area, PhD is a plus, or equivalent experience.
3+ years of experience with exposure to performance analysis and complex system on chip and/or GPU architectures.
Strong understanding of System-on-Chip (SoC) architecture, graphics pipeline, memory subsystem architecture and Network-on-Chip (NoC)/Interconnect architecture.
Expert hands on competence in programming (C/C++) and scripting (Perl/Python). Exposure to Verilog/System Verilog, SystemC/TLM is a strong plus.
Strong debugging and analysis (including data and statistical analysis) skills, including use for RTL dumps to debug failures.
Hands on experience developing performance simulators, cycle accurate/approximate models for pre-silicon performance analysis is a strong plus.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative, autonomous and love a challenge, we want to hear from you. Come, join our Deep Learning Automotive team and help build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
#LI-Hybrid
Skills Required
- 3+ years of experience in performance analysis of complex SoCs or GPU architectures.
- Strong knowledge of System-on-Chip architecture and graphics pipeline.
- Expert hands-on experience in C/C++ programming and scripting in Perl/Python.
- Experience with Verilog/System Verilog or SystemC/TLM.
- Strong debugging and statistical analysis skills.
- Hands-on experience with performance simulators and models for pre-silicon analysis.
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.”
