ASIC Design Engineer
NVIDIA
ASIC Design Engineer
NVIDIA
Designs the chips and software that power AI
The clocks group is looking for a top-notch ASIC engineer to join the team. The Team is responsible for crafting all aspects of GPU and CPU clocking. The team collaborates with the front design team to understand the clocking requirements for the chip. The clocks team interacts with the floor-planning and back end team to help craft the physical floorplan of the chip. The team explains the programming model to the SW team to come up with an efficient clock programming sequence. The team works with the silicon solution team to triage silicon or programming bugs in the lab.
NVIDIA has continuously reinvented itself over two decades. Our 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. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can take on, and that matter to the world. This is our life’s work, to amplify human creativity and intelligence. Make the choice to join us today.
What you'll be doing:
As a Clocks team member, you will be architecting the clock domain to satisfy functional, physical and testing design requirements.
Engage with multiple teams and design the GPU or CPU clocks to satisfy all the architectural/design/physical constraints.
Improve Power, Performance, and Area (PPA) of innovative NVIDIA chips by evaluating trade-offs across DFx, Physical Implementation, Power Optimization and Ease of timing closure to innovate and implement new Clocking topologies in RTL.
Collaborate with Physical design and timing team to evaluate Clocking concerns and develop solutions for supporting high speed Clocking.
Together with other team members, we deliver clock RTL information to GPU, CPU and SOC verification team, timing and DFT teams.
Get involved in end-to-end cycle of ASIC execution starting from micro-arch, design implementation, design fixes, sign-off checks and all the way to Silicon bringup.
What we need to see:
BS in Electrical Engineering or equivalent experience (MS preferred)
2+ years of relevant work experience.
Deep understanding of logic optimization techniques and PPA trade-offs.
Excellent interpersonal skills and ability to collaborate with multiple teams.
Experience in RTL design (Verilog), verification and logic synthesis.
Strong coding skills in python or other industry-standard scripting languages.
Understanding of sub-micron silicon issues like noise, cross-talk, and OCV effects is a plus.
Implementing on-chip clocking networks is a bonus
Ways to stand out from the crowd:
Experience with clocks controller, clocks logic design
Understanding of system level artifacts like power, noise, etc
Experience with scalable designs and architecture.
Hands- on silicon debug is a plus.
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most brilliant people in the world working for us and, due to unprecedented growth, our teams are rapidly growing. Are you passionate about becoming a part of a best-in-class team supporting the latest in GPU and AI technology? If so, we want to hear from you.
#LI-Hybrid
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.NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.
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