Senior System GPU Performance Engineer

Job Title

Senior System GPU Performance Engineer

Role Summary

The Senior System GPU Performance Engineer will maximize performance and power-efficiency of production GPU systems by connecting workload behavior, silicon capability, software policy, and platform constraints to identify bottlenecks and productize improvements.

This role sits within NVIDIA's Silicon Co-Design Group (SCG) and owns analysis from hypothesis through root-cause closure, plan-of-record integration, and confirmed product impact.

Experience Level

Senior - typically requires 8+ years of relevant experience in GPU or system performance engineering, post-silicon characterization, silicon productization, or hardware-software performance optimization.

Responsibilities

Primary responsibilities include system-level characterization, driving productization, experimental design, and cross-functional closure.

• Own system-level GPU performance and power characterization from first silicon through production across representative applications, benchmarks, and product configurations.
• Drive performance and power feature productization; translate measured behavior into firmware, driver, BIOS, platform, and silicon recommendations that meet product targets and schedules.
• Design experiments, execute test plans, and build models to isolate bottlenecks across GPU compute, memory, interconnect, CPU interaction, power delivery, and thermal limits.
• Analyze production-silicon data across process, voltage, temperature, workloads, and bins to quantify performance-per-watt trade-offs and identify optimization opportunities.
• Lead multi-functional root-cause closure across architecture, design, validation, software/firmware, power/thermal, reliability, ATE, product management, manufacturing, and operations; own fixes through confirmation.
• Establish reusable automation, visualization, and closed-loop methodologies to improve experiment coverage, analysis accuracy, debug velocity, and learning across programs.
• Translate complex system signals into decision-ready options for executive leadership on feature readiness, product configuration, targets, and program risks.

Requirements

Must-have technical skills, experience, and behaviors for the role.

• 8+ years of experience in GPU or system performance engineering, post-silicon characterization, silicon productization, or hardware-software performance optimization.
• Hands-on experience with silicon bring-up, frequency and power characterization, product binning, and performance-per-watt optimization across process, voltage, temperature, workloads, and system configurations.
• Strong understanding of GPU and system architecture, including compute pipelines, memory hierarchy, interconnects, CPU-GPU interactions, scheduling, telemetry, and sustained-performance limits.
• Proven ability to design controlled experiments, develop performance or power models, analyze large datasets, and use statistics to separate bottlenecks and causal effects from noise.
• Strong programming and analysis skills in Python plus one or more of C, C++, SQL, JMP, or equivalent; experience automating tests, data processing, and visualization.
• Demonstrated ability to structure ambiguous system-level problems and drive them to root-cause closure across globally distributed, multi-functional teams.
• Strong written and verbal communication; able to translate complex technical issues into concise, decision-ready options for leadership.

Nice-to-have:

• Track record of shipping GPU performance or power features that improved application performance, performance-per-watt, product segmentation, or time to market.
• Experience optimizing large GPU, CPU, AI accelerator, or complex SoC platforms for Datacenter, Gaming, Automotive, Robotics, or Embedded products.
• Deep experience with GPU profiling, workload characterization, production telemetry, performance counters, or simulation-to-silicon correlation.
• Experience building reusable performance models, test frameworks, or analysis methodologies used across multiple silicon programs or advanced process nodes.
• Experience applying AI tools to accelerate experiment design, anomaly detection, debug, analysis, or reporting while maintaining correctness guardrails.

Education Requirements

BS or MS in Electrical Engineering, Computer Engineering, Computer Science, Systems Engineering, or a related field - or equivalent practical experience.

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.