Distinguished Engineer, End-to-End Scaling Performance Architecture
We are looking for a Distinguished Engineer to join NVIDIA's architecture organization and help define how future accelerated computing systems scale from a single processor to multi-die, multi-GPU, and multi-node platforms! In this role, we will rely on you to set long-term performance strategy across applications, systems, and architecture, including DRAM, NVLink, and chip-to-chip (C2C) interconnects.
We focus this role on architectural direction and application outcomes. We need someone who can identify where data movement, communication, memory behavior, topology, and compute limit scaling, then turn those insights into priorities that guide multiple product generations. Our domain teams own detailed implementation and delivery. We will count on you to align their decisions around a shared end-to-end strategy so local improvements create meaningful system-level gains.
What You Will Be Doing:
- Ask you to define the multi-generation strategy for application scaling across DRAM, NVLink, C2C, compute, and the supporting software stack.
- Translate the behavior of important AI, HPC, and accelerated computing applications into architectural requirements, performance targets, and investment priorities.
- Rely on you to build a clear view of how bottlenecks shift as workloads scale across dies, GPUs, nodes, model sizes, data sets, and communication patterns.
- Evaluate system-level trade-offs across bandwidth, latency, capacity, topology, coherence, power, area, cost, programmability, and resiliency.
- Use your leadership to establish common workload scenarios, scaling metrics, models, and decision frameworks so architecture teams can compare proposals against application outcomes.
- Count on you to identify architectural discontinuities and emerging technology opportunities early enough to shape product and technology decisions. We will partner with you to align DRAM, NVLink, C2C, GPU, CPU, system, and software architects around shared performance limits and high-value opportunities. You will work with application, framework, compiler, runtime, modeling, and post-silicon teams to connect measured behavior with future architecture choices.
- Value your clear recommendations to senior technical and business leaders, including assumptions, sensitivities, risks, and expected impact. We will also ask you to mentor system performance architects, strengthen technical communities across teams, generate sustained intellectual property, and help influence the direction of large-scale accelerated computing.
What We Need To See
- MSEE, MSCE, PhD, or equivalent experience in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
- 18+ years of relevant industry or academic experience, including experience setting architecture direction for complex, high-performance systems.
- Deep understanding of system performance and scaling, including interactions among DRAM behavior, high-bandwidth fabrics such as NVLink, and C2C communication.
- Strong application-level intuition, including the ability to connect workload algorithms, parallelism, communication, locality, and data movement to architecture choices and measurable outcomes. We value experience with workload characterization, analytical or simulation-based performance modeling, bottleneck analysis, and architecture trade-off evaluation.
- We look for a record of identifying cross-domain opportunities that may not be visible when teams optimize individual components separately. We need demonstrated ability to create and advance a multi-generation technical strategy through influence across silicon, systems, software, and application teams.
- We value clear communication and sound judgment in ambiguous technical areas, with the ability to explain complex system trade-offs to specialists and executive leaders. We also look for experience mentoring senior engineers into broader architecture leadership roles and building strong technical communities.
Ways To Stand Out from the crowd:
- If you have shaped product or technology roadmaps around application-level scaling needs, helped architecture teams build a quantitative understanding of bottlenecks across memory, interconnect, compute, and software, or identified high-impact trade-offs early enough to guide product, architecture, or technology investment.
- We will also value examples where your work delivered measurable end-to-end improvements in performance, efficiency, or scaling for priority applications.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 320,000 USD - 488,750 USD.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 1, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive 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.
About NVIDIA
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI.
More jobs at NVIDIA
- Software Engineer, SRE and Production Engineering - DGX Cloud
- C++ Software Engineer, Infrastructure Tools - New College Grad 2027
- ASIC Clocks Verification Engineer - New College Grad 2026
- Senior Deep Learning Software Engineer, Inference and Model Optimization
- Research Intern, World Models and Synthetic Data for Autonomous Driving - Summer 2027
- Director, Software Engineering – Cloud Compute and Infrastructure
- Senior AI Engineer, AI tools
- Senior Deep Learning Compiler Engineer
- Hardware Design Validation Engineer - Memory Subsystem - New College Grad 2026
- Senior System Software Engineer, Replay and Simulation