NVIDIA builds accelerated computing platforms that are redefining AI, data centers, autonomous systems, and scientific computing. The Global Flows Team advances RTL quality and sign-off methodologies used across NVIDIA CPU programs. This position connects digital implementation and microarchitecture with CDC, RDC, RTL-NA/DA, PSI and lint. The work spans methodology development, technical analysis, tool deployment, and coordination across development and CAD organizations. Results improve RTL quality, project predictability, and readiness for silicon.
What you’ll be doingAs an ASIC Engineer, you will embark on an ambitious journey to develop and deploy innovative RTL design-quality methodologies. These include CDC, RDC, Netlist/Design Auditors, Power Insertion, lint, and related structural checks. Your role will involve:
Analyzing flow results in the context of design intent and identifying root causes across RTL, constraints, tools, and methodology.
Establishing quality requirements and readiness criteria for project achievements while monitoring regressions, violation trends, and sign-off status.
Collaborating across RTL build, DFT, clocks, reset, low-power, physical-design, verification, and CAD teams to resolve technical issues.
Assessing new tool releases and methodology capabilities, coordinating their integration into project environments.
Improving runtime, debug efficiency, waiver management, reporting, and training through automation, reusable scripts, and engineering guidelines.
We are seeking a professional with:
A Bachelor’s degree in Electrical Engineering, Computer Engineering, or a related field, or equivalent experience.
3+ years of proven experience in ASIC or SoC RTL design or design-methodology.
Practical experience with Verilog or SystemVerilog build, analysis, and debugging.
Knowledge of digital design and microarchitecture, including pipelines, finite-state machines, clock/reset architectures, synchronizers, handshakes, asynchronous FIFOs, metastability, and power-domain crossings.
Experience with front-end methodologies such as lint, CDC/RDC, structural RTL analysis, low-power verification, synthesis, or timing constraints.
The ability to relate methodology results to RTL behavior and perform detailed root-cause analysis.
Experience with Linux development environments and scripting in Python, Tcl, Perl, shell, or a comparable language.
To shine in this role, you may possess:
Experience with RTL integration, design bring-up, or multi-team debugging at the unit, subsystem, chiplet, or SoC level.
Familiarity with tools such as Meridian, SpyGlass, VC CDC, Questa CDC, Design Auditor, Power insertion, or equivalent.
Knowledge of UPF and low-power RTL implementation or verification.
Experience with SDC, synthesis, static timing analysis, formal verification, or equivalence checking.
Join NVIDIA and help build the future of technology. Your efforts will drive us toward our demanding targets and build a meaningful legacy in the world. This is your chance to be part of a world-class team and compete at the highest level!
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, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Skills Required
- Bachelor's degree in Electrical Engineering, Computer Engineering, or related field (or equivalent experience)
- 3+ years of ASIC or SoC RTL design or design-methodology experience
- Practical experience with Verilog or SystemVerilog build, analysis, and debugging
- Knowledge of digital design and microarchitecture (pipelines, FSMs, clock/reset, synchronizers, async FIFOs, metastability, power-domain crossings)
- Experience with front-end methodologies such as lint, CDC/RDC, structural RTL analysis, low-power verification, synthesis, or timing constraints
- Ability to relate methodology results to RTL behavior and perform detailed root-cause analysis
- Experience with Linux development environments
- Scripting experience in Python, Tcl, Perl, shell, or comparable language
- Experience with RTL integration, design bring-up, or multi-team debugging at unit/subsystem/SoC level
- Familiarity with tools such as Meridian, SpyGlass, VC CDC, Questa CDC, Design Auditor, Power insertion (or equivalents)
- Knowledge of UPF and low-power RTL implementation or verification
- Experience with SDC, synthesis, static timing analysis, formal verification, or equivalence checking
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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