CUDA Systems Engineer - Performance Driver & Runtime

NVIDIA

California (MO)

On-site

USD 124,000 - 196,000

Full time

4 days ago

Be an early applicant

Get more replies from employers

Send a job-specific resume in minutes.

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA seeks systems software engineers to design and ship production C/C++ features in the CUDA driver and runtime, optimizing critical paths across CPU-GPU boundaries. You will bring up new platforms, analyze workloads, and drive hardware/software direction to accelerate AI and HPC workloads.

You will work with cross-functional teams, establish performance targets for current and future platforms, and translate findings into API and architecture improvements, shaping the next generation of

Qualifications

BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related field—or equivalent practical experience - with at least 2 years of relevant systems-software development experience.
Strong production C/C++ systems-programming experience, including delivery of substantial features, optimizations, or production fixes in a complex codebase.
Strong operating systems and concurrency foundations, including threads, synchronization, processes, virtual memory, and user/kernel interactions.
Strong computer-architecture foundations, including processors, memory hierarchy, caching and coherence, data movement, and system interconnects.
Demonstrated success improving real software performance: measuring behavior, identifying the limiting mechanism, implementing an effective solution, and validating the result quantitatively.
Sound technical judgment, ownership of ambiguous problems, and clear communication across organizational and disciplinary boundaries.
Direct CUDA or GPU experience is valuable but is not required when accompanied by deep systems-software, operating-systems, computer-architecture, and performance-engineering foundations.

Responsibilities

Design, implement, validate, and ship performance-centric features and programming-model capabilities in the CUDA driver and runtime, writing maintainable, well-tested production C/C++.
Optimize critical execution paths- including kernel launch, synchronization, memory management & movement, CPU-GPU coordination, and system interconnect use for latency, throughput, bandwidth, efficiency, and scalability.
Own complex performance problems end-to-end - understand important workloads, form hypotheses, create focused measurements and models, isolate root causes across software and hardware boundaries, implement production solutions, and validate application-level impact.
Establish performance expectations for current and future platforms, characterize new silicon, close software and hardware gaps, and drive performance readiness through product release.
Translate workload and platform evidence into CUDA API and programming-model improvements, systems-software direction, and measurement-backed recommendations for future hardware architecture and implementation.
Partner with application, library, framework, operating-system, driver, runtime, firmware, GPU architecture, silicon, product, and customer-facing teams; communicate findings clearly and raise engineering quality through design and code reviews.
Lead complex feature development and cross-layer investigations across teams, define performance requirements and technical direction for major subsystems, mentor engineers, and shape hardware/software decisions for future product generations.