Server Performance Architect - Hardware

NVIDIA; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

NVIDIA has continuously reinvented itself and drives AI server workloads at scale from Hyderabad. We seek an architect to push architectural performance for next-generation AI server systems, bridging deep theory with hands-on silicon investigations and benchmarking across NVIDIA and competitive platforms.

You’ll define targets for CPU, GPU, memory, interconnect, and storage subsystems, perform workload characterisation, and develop models to guide decisions.

Qualifications

• BS, MS, or PhD in Electrical/Computer Engineering, Computer Science, or equivalent experience.
• 10+ years of experience in server/system performance architecture.
• Deep understanding of modern server architectures — CPU microarchitecture, PCIe/CXL, DDR/HBM memory subsystems, and coherency protocols.
• Strong hands-on experience with system-level profiling and performance analysis tools on server platforms.
• Excellent communication skills for distilling performance data into actionable architectural recommendations.

Responsibilities

• Define and drive server-level performance targets across CPU, GPU, memory, interconnect, networking, and storage subsystems.
• Conduct hands-on workload characterisation and bottleneck analysis on NVIDIA and competitive server platforms using AI training, inference, and HPC benchmarks.
• Leverage profiling, tracing, and analysis tools to root-cause performance issues and identify optimisation opportunities at the system level.
• Perform trade-off studies on system topology, thermal/power envelopes, and memory hierarchy to guide architectural decisions.
• Collaborate with silicon, platform, firmware, and software teams to identify and close performance gaps from bring-up through production.
• Develop automation and tooling for performance regression tracking and reporting.
• Represent the performance perspective in architecture reviews and cross-functional design discussions.
• Build and maintain analytical performance models and simulation frameworks for next-generation server platforms.
• Publish internal performance studies and best-practice guides for partner and customer enablement.

Skills

Python

C/C++

GPU-accelerated compute

System-level profiling

CPU microarchitecture

PCIe/CXL

Memory subsystems

Communication

AI tooling familiarity

Education

BS/MS/PhD in Electrical/Computer Engineering or Computer Science

Tools

Profiling tools

Job description

Job Description:

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!

NVIDIA is seeking architects to drive architectural performance for its next-generation AI server systems. This position demands a unique capability to bridge deep architectural knowledge, workload analysis, and hands‑on silicon investigations. Candidates should be adept at working directly with silicon, high‑level models, and simulators. Responsibilities include conducting performance investigations on both NVIDIA and competitive platforms, and developing targeted microbenchmarks to examine specific architectural aspects. The role does not heavily involve modeling tasks (functional or performance), though occasional focused assignments may arise.

What You’ll Be Doing

• Defining and driving server‑level performance targets across CPU, GPU, memory, interconnect, networking, and storage subsystems
• Conducting hands‑on workload characterisation and bottleneck analysis on NVIDIA and competitive server platforms using AI training, inference, and HPC benchmarks
• Leveraging profiling, tracing, and analysis tools to root‑cause performance issues and identify optimisation opportunities at the system level
• Performing trade‑off studies on system topology, thermal/power envelopes, and memory hierarchy to guide architectural decisions
• Collaborating with silicon, platform, firmware, and software teams to identify and close performance gaps from bring‑up through production
• Developing automation and tooling for performance regression tracking and reporting
• Representing the performance perspective in architecture reviews and cross‑functional design discussions
• Building and maintaining analytical performance models and simulation frameworks for next‑generation server platforms
• Publishing internal performance studies and best‑practice guides for partner and customer enablement

What We Need To See

• BS, MS, or PhD in Electrical/Computer Engineering, Computer Science, or equivalent experience
• 10+ years of experience in server/system performance architecture or related disciplines
• Deep understanding of modern server architectures — CPU microarchitecture, PCIe/CXL, DDR/HBM memory subsystems, and coherency protocols
• Strong hands‑on experience with system‑level profiling and performance analysis tools on server platform(s)
• Solid knowledge of GPU‑accelerated compute, high‑performance networking, or high‑performance storage subsystems
• Proficiency in Python, C/C++, or similar languages for scripting, data analysis, and tool development
• Comfortable and proficient using AI‑powered coding and productivity tools to accelerate analysis, automation, and documentation workflows
• Excellent communication skills with the ability to distil complex performance data into actionable architectural recommendations

Ways To Stand Out From The Crowd

• Experience with AI/ML training and inference workloads at data‑centre scale
• Familiarity with NVIDIA GPU architectures (Hopper, Blackwell, Rubin) and associated software stacks (CUDA, NCCL)
• Background in chip‑to‑chip interconnect performance analysis (C2C, UCIe)
• Exposure to power/thermal‑aware performance optimisation techniques
• Track record of contributions to industry conferences or published performance studies