Nvidia
System Software Engineer, Performance - CUDA Driver
US, CA, Santa Clara
Sponsorship not specifiedDetected 4 days ago
PythonC++CDeep LearningData AnalysisA/B TestingRoboticsElectrical EngineeringResearchCommunication
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odds of building a lasting career here
42Risky
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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from this employer's Department of Labor filings
Green-card filing pattern in this occupation
Context, not a finding about this posting: of this employer's 399 green-card filings in this occupation, 96% were for a worker who already held the job.
Green-card intent detected
NVIDIA Corporation obtained a prevailing wage determination for Software Developers in Santa Clara, CALIFORNIA on 2026-06-08. No matching green-card filing appears in our data yet. The determination expires in 0 days (2026-09-05), and a green-card filing must follow before then or the employer starts over.+2 more active determinations on file
Green-card follow-through: 92%
Of 382 labor certifications old enough to have been used, 29 expired without the employer filing the next step. Median time from filing to decision: 497 days.97% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.
Files H-1B transfers
620 transfer filings in the last year, covering 1241 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
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About the role
- The AI revolution is not powered by models alone, rather it advances when enormous amounts of computation become fast, efficient, and economical enough to turn new ideas into products people can use on a global scale.
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.
- 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.
- Evidence of technical invention(s), such as software-performance patents, novel production designs, or measurement-backed recommendations that influenced a hardware revision or future architecture.
- One well-designed systems feature can help customers obtain more useful work from GPUs already deployed while informing how future CUDA capabilities and GPU architectures are designed.
Requirements
- 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.
- Experience with pre-silicon analysis, platform bring-up, performance modeling, or hardware/software co-design.
- Systems-level performance experience with AI/DL, HPC, graphics, automotive, robotics, or similarly demanding workloads.
Skills
- Faster training lets research and product teams test the next idea sooner.
- Lower-latency, higher-throughput inference makes AI assistants and agents more responsive and practical for more people.
- Shorter time to solution lets scientists and engineers explore more possibilities within the same time and energy budget.
- At NVIDIA, performance is not a supporting metric - it is how architectural invention becomes useful computing.
Compensation
- Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
- The base salary range is 124,000 USD - 195,500 USD.
Company info
- We are looking for systems software engineers who want to work at this leverage point.
- Ways to stand out from the crowd: Experience developing GPU or accelerator drivers, runtimes, kernel software, firmware, compilers, or other performance-critical low-level systems.
Equal opportunity
- NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer.
This listing is sourced directly from Nvidia's careers page and normalized into a canonical job model.