2 days ago
Base Salary
$184k - $357k/yr
Responsibilities
- Analyze and optimize performance on current and next-generation NVIDIA GPUs.
- Create and optimize parallel algorithms, data structures, and reference code for NVIDIA GPUs.
- Analyze hardware and software architecture interactions across algorithms, programming models, and applications.
- Collaborate with hardware design, software engineering, product, and research teams to guide accelerated computing direction.
- Investigate accelerated computing applications to support software-hardware co-design.
- Write and present white papers, conference publications, blog posts, patent applications, and related technical materials.
Requirements
- MS or PhD in Computer Science, Computer Engineering, or Electrical Engineering, or equivalent experience.
- 6+ years of relevant work experience.
- Strong mathematical fundamentals, including linear algebra and numerical methods.
- Hands-on experience with massively parallel GPU programming models such as CUDA or OpenCL.
- Strong knowledge of C and C++ and understanding of software design, programming techniques, and algorithms.
- Passion for performance optimization and strong communication, organization, problem-solving, time-management, and prioritization skills.
- Familiarity with MPI, OpenSHMEM/NVSHMEM, multicore CPU threading APIs, Unix-style IPC APIs, and Python is a plus.
Benefits
- Eligible for equity and benefits.
- Base salary varies by location, experience, and other objective job-related factors.
- Applications will be accepted at least until November 3, 2026.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.