We are looking for a highly skilled Performance Modeling Architect to lead the architectural definition and improvement of our next-generation CPU Cache Hierarchies and interconnects. This is an outstanding chance to create scalable solutions that connect two fast-paced domains: the high-reliability, low-latency needs of Automotive and the massive efficiency, high-density demands of Data Center systems. You will build the "source of truth" models that govern data movement across our silicon, ensuring our next-level caches (L3/System Cache) and coherent fabrics achieve ambitious performance goals.
What you'll be doing:
As a core member of the architecture team, your daily work will involve:
Developing and maintaining high-fidelity, cycle-accurate performance models (C++/SystemC) for coherent interconnects and large-scale shared caches.
Modeling and analyzing performance bottlenecks across varying scales, from small-cluster automotive SoCs to massive, multi-mesh data center architectures.
Evaluating the performance impact of different coherency protocols (e.g., CHI, ACE, or proprietary) and snooping filters.
Running and analyzing industry-standard benchmarks (SPEC, MLPerf, Automotive-specific suites) to drive architectural trade-offs.
Collaborating with build and verification teams to correlate performance models with silicon and working with software teams to optimize drivers for the underlying hardware topology.
What we need to see:
To be successful in this role, you should possess a deep technical foundation in computer architecture:
A Master’s or Ph.D. in Computer Engineering, Electrical Engineering, or Computer Science (or equivalent experience) with a focus on architecture with 5+ years of experience.
Strong understanding of CPU microarchitecture, memory consistency models, and cache coherency protocols.
Proven experience in C++ or SystemC for cycle-accurate or functional modeling.
Proficiency in Python or similar scripting languages for processing large datasets, generating performance visualizations, and automating simulation sweeps.
Understanding of Network-on-Chip (NoC) topologies (Mesh, Ring, Torus), credit-based flow control, and arbitration logic.
Ways to stand out from the crowd:
We are looking for individuals who bring a "systems-thinking" approach to hardware development. You will stand out if you have:
Cross-Domain Versatility: Practical experience managing the functional safety (ISO 26262) requirements of automotive chips alongside the power-performance-area (PPA) limitations of data center hardware.
Hardware Performance Counters: Experience defining or using PMU (Performance Monitoring Unit) events to debug performance on real silicon or emulators.
Formal Methods: A background in using formal verification or mathematical modeling to prove the correctness of complex coherency state machines.
Custom Tooling: A history of building your own internal tools or frameworks to accelerate architectural exploration rather than just using off-the-shelf simulators.
Advanced Memory Systems: Knowledge of emerging memory technologies like CXL (Compute Express Link) or HBM (High Bandwidth Memory) and how they collaborate with coherent fabrics.
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 a diverse 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.Skills Required
- Master's or Ph.D. in Computer Engineering, Electrical Engineering, or Computer Science
- 5+ years of experience in computer architecture
- Strong understanding of CPU microarchitecture, memory consistency models, and cache coherency protocols
- Proven experience in C++ or SystemC for modeling
- Proficiency in Python or similar scripting languages
- Understanding of Network-on-Chip (NoC) topologies
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.”
