NVIDIA has transformed computer graphics, PC gaming, and accelerated computing for more than 25 years. Today, our GPUs power advances in AI, Datacenter, Gaming, Robotics, Automotive, and scientific discovery. NVIDIA's Silicon Co-Design Group (SCG) takes GPU, SoC, and CPU programs from first power-on to high-volume production. We sit at the crossroads of architecture, design, marketing, operations, and productization across Datacenter, Gaming, Robotics, Automotive, and Embedded markets.

We are hiring a Senior System GPU Performance Engineer to maximize the performance and power efficiency of production GPU systems. You will connect workload behavior, silicon capability, software policy, and platform constraints to identify bottlenecks and productize improvements. This is not a benchmark-execution or validation-only role—you will own analysis from hypothesis through root-cause closure, plan-of-record integration, and confirmed product impact. Great work turns complex system data into faster, more efficient, and more predictable products.

What you'll be doing:

• 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, translating measured behavior into firmware, driver, BIOS, platform, and silicon recommendations that meet product targets and speed-of-light schedules.
• Design experiments, execute test plans, and build models that 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 causal optimization opportunities.
• Lead multi-functional root-cause closure across architecture, design, validation, software/firmware, power and thermal, reliability, ATE, product management, manufacturing, and operations; own fixes through confirmation.
• Establish reusable automation, visualization, and closed-loop methodologies that improve experiment coverage, analysis accuracy, debug velocity, and learning across future GPU programs.
• Translate complex system signals into decision-ready options for executive leadership on feature readiness, product configuration, targets, and program risks.

What we need to see:

• BS or MS in Electrical Engineering, Computer Engineering, Computer Science, Systems Engineering, or related field (or equivalent experience).
• 8+ overall 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 using Python and one or more of C, C++, SQL, JMP, or equivalent, with 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 hardware and software teams.
• Strong written and verbal communication; able to translate complex technical issues into crisp, decision-ready options for executive leadership.

Ways to stand out from the crowd:

• Track record of shipping GPU performance or power features that measurably improved application performance, performance per watt, product segmentation, or time to market.
• Experience optimizing large GPU, CPU, AI accelerator, or other 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 adopted across multiple silicon programs or advanced process nodes.
• Applied AI tools to accelerate experiment design, anomaly detection, debug, analysis, or reporting workflows and can describe the measurable outcome and the guardrails used to protect correctness.

NVIDIA is the world leader in accelerated computing, powering AI, gaming, robotics, autonomous systems, and scientific discovery. We invest in our people with competitive benefits, continuous learning, and a team where everyone can do their best work.

NVIDIA is committed to fostering a diverse work environment and is proud to be an equal opportunity employer. We do not discriminate on the basis of race, color, national origin, gender, gender identity, sexual orientation, religion, age, marital status, veteran status, disability, or any other legally protected status.