Senior Accelerated Computing Architect

at Nvidia

USD 184,000-356,500 per year

SENIOR

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI API @ 3 Algorithms @ 4 CUDA @ 4 Communication @ 3 Data Structures GPU @ 4 HPC MPI @ 3 Machine Learning OpenCL @ 4 Performance Optimization @ 6 Prioritization @ 6 Python @ 3

Details

We are now looking for a Senior Accelerated Computing Architect.

NVIDIA is developing software and system architectures for accelerated high performance computing, scientific computing, machine learning, AI, datacenter, and automotive computing. This position offers you the opportunity to make a meaningful impact in a fast-moving, technology focused company.

Responsibilities

Performing in-depth analysis and optimization to ensure the best possible performance on current and/or next-generation NVIDIA GPUs.
Creating and optimizing core parallel algorithms, data structures, and reference codes to provide the best possible solutions for NVIDIA GPUs.
Understanding and analyzing the interplay of hardware and software architectures on core algorithms, programming models, and applications.
Actively collaborating with the hardware design, software engineering, product, and research teams to guide the direction of accelerated computing.
Diving into accelerated computing applications to facilitate software-hardware co-design.
Writing up and presenting your work by writing white papers, conference publications, official blog posts, patent applications, etc. as appropriate.

Requirements

An MS or Ph.D. 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.
A passion for performance optimization.
Hands-on experience with the massively parallel GPU programming model, e.g. CUDA or OpenCL. Familiarity with APIs for multi-node communication, like MPI or OpenSHMEM/NVSHMEM, is a plus.
Strong knowledge of C and C++ with solid understanding of software design, programming techniques, and algorithms. Familiarity with threading APIs for multicore CPUs and Unix-style Inter-process Communication (IPC) APIs is a plus.
Familiarity with Python is a plus.
Good communication and organization skills, with a logical approach to problem solving, good time management, and task prioritization skills.