The Role
Design and validate transistor-level embedded SRAM macros, supervise layout and verification, develop/automate SRAM compiler flows, explore process-node optimizations for power/performance/area, and collaborate with SoC, silicon test, and productization teams to deliver production-ready silicon.
Summary Generated by Built In
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!
The Digital IP (DIP) group at NVIDIA is an organization of circuit design and CAD engineers that creates a wide variety of IP for the chips NVIDIA designs. This group works closely with internal SOC design, silicon testing, and productization teams that turn our IP into products that change the world. One of the roles fulfilled by the DIP group is to develop sophisticated SRAM compilers that are used extensively by our SOC design partners. We are looking to hire a skilled and creative SRAM circuit designer to help achieve these goals in a high-visibility position.
What you'll be doing:
The Digital IP (DIP) group at NVIDIA is an organization of circuit design and CAD engineers that creates a wide variety of IP for the chips NVIDIA designs. This group works closely with internal SOC design, silicon testing, and productization teams that turn our IP into products that change the world. One of the roles fulfilled by the DIP group is to develop sophisticated SRAM compilers that are used extensively by our SOC design partners. We are looking to hire a skilled and creative SRAM circuit designer to help achieve these goals in a high-visibility position.
What you'll be doing:
- Embedded SRAM design: Transistor-level circuit design, supervising layout implementation, physical and logical verification, and debug of SRAM macros.
- SRAM compiler development: Envisioning, defining, and coding more efficient ways to automate the simultaneous assembly and validation of multiple unique SRAM macros using NVIDIA's extensive compute resources.
- Advanced development: Exploring the potential of future process nodes and developing techniques to achieve optimal power, performance, and area characteristics.
- Guiding SOC design, silicon test, and productization efforts: Collaborate with SOC design partners to help them achieve their overall performance and cost goals, guide the silicon test and characterization efforts, and working with productization teams to prepare new silicon for the demanding requirements of real-world applications.
What we need to see:
- BSEE minimum (or equivalent experience), MSEE or PhD preferred
- 2+ years of SRAM design experience with a strong background in digital circuit design, layout, and validation on advanced FinFET processes
- Prior design experience in single-port, dual-port, or register file SRAM-based macros required, including complex circuits like self-timed logic and sense-amplifiers
- Python scripting ability to parse data and automate tasks
- Successful track record of delivering designs to production
Ways to stand out from the crowd:
- Self-motivation, attention to detail, and good written, verbal, and presentation skills are needed to success in this role
- A high degree of scripting expertise in Python
- Familiarity with Cadence schematic and layout capture tools
- Silicon testing/debug experience
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 imagination and intelligence. Make the choice, join our diverse team today!
#LI-Hybrid
#LI-Hybrid
Skills Required
- BSEE or equivalent experience
- MSEE or PhD
- 2+ years SRAM design experience with digital circuit design, layout, and validation on advanced FinFET processes
- Prior design experience in single-port, dual-port, or register-file SRAM macros, including self-timed logic and sense-amplifiers
- Python scripting ability to parse data and automate tasks
- Successful track record of delivering designs to production
- High degree of scripting expertise in Python
- Familiarity with Cadence schematic and layout capture tools
- Silicon testing and debug experience
- Self-motivation, attention to detail, and strong communication/presentation skills
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.
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The Company
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.”
