GPU Architect

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!

What you'll be doing :

Performance analysis / bottleneck analysis of complex, high performance GPUs and System-on-Chips (SoCs).

Work on hardware models of different levels of extraction, including performance models, RTL test benches and emulators, to find performance bottlenecks in the system.

Work closely with the architecture and design teams to explore architecture trade-offs related to system performance, area, and power consumption.

Understand key performance usecases or the product. Develop workloads and test suits targeting graphics, machine learning, automotive, video, compute vision applications running on these products.

Drive methodologies for improving turnaround time, finding representative data-sets and enabling performance analysis early in the product development cycle.

Develop required infrastructure including performance simulators, testbench components and analysis tools.

What we need to see :

BE / BTech or MS / MTech in relevant area, PhD is a plus.

2+ years of experience with exposure to performance analysis and complex system on chip and / or GPU architectures.

Demonstrated history of technical leadership.

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.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most brilliant and talented 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

System Verilog, Verilog, C, Systemc, Python, Perl, Tlm