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NVIDIA

Designs the chips and software that power AI

Senior Accelerated Computing Architect

Systems Software EngineeringUpdated on Oct 31
Full-timeIn-person

About the Job

Requirements

  • An MS or Ph.D. in Computer Science, Computer Engineering, or Electrical Engineering, or equivalent experience.
  • At least 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++, including software design, programming techniques, and algorithms.
  • Good communication and organization skills, a logical approach to problem solving, good time management, and task prioritization skills.

Responsibilities

  • Perform in-depth analysis and optimization to ensure the best possible performance on current and/or next-generation NVIDIA GPUs.
  • Create and optimize core parallel algorithms, data structures, and reference codes to provide the best possible solutions for NVIDIA GPUs.
  • Understand and analyze the interplay of hardware and software architectures on core algorithms, programming models, and applications.
  • Collaborate with hardware design, software engineering, product, and research teams to guide the direction of accelerated computing.
  • Dive into accelerated computing applications to facilitate software-hardware co-design.
  • Write and present work through white papers, conference publications, official blog posts, patent applications, and other appropriate formats.

Desired Qualifications

  • Familiarity with APIs for multi-node communication, such as MPI or OpenSHMEM/NVSHMEM.
  • Familiarity with threading APIs for multicore CPUs and Unix-style Inter-process Communication (IPC) APIs.
  • Familiarity with Python.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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