Senior DevTech: GPU-Accelerated Data Compression & Processing

NVIDIA; California (MO); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA seeks a Senior DevTech Compute Engineer for Compression and Data Processing to prototype GPU-accelerated distributed data processing, including dataframe analytics and lossless/lossy compression methods. You will work with experts across industry and academia to optimize data-intensive workloads on GPU/CPU architectures.

The role involves collaboration with research, hardware, and software teams, influencing hardware/software co-design, and delivering solutions to top customers.

Qualifications

• Masters or PhD in Computer Science, Computer Engineering, Applied Math and/or related computationally focused science degree.
• At least 5+ years of relevant work or research experience.
• Hands-on experience with low-level parallel programming across execution units (CPU/GPU/NPU/ASICs).
• Fluency in C/C++, algorithms and data structures.
• Domain expertise in data processing, compression and decompression, codecs or high performance distributed databases.

Responsibilities

• Prototype and integrate novel approaches to GPU-accelerated distributed data processing domains.
• Collaborate with researchers and industry to optimize complex data-intensive workloads.
• Influence design of next-generation hardware architectures, software, and programming models.
• Work with NVIDIA largest customers to integrate solutions and influence standards.

Skills

C/C++ programming

Parallel programming

Distributed data processing

Data analysis

Education

Master's or PhD in Computer Science/Engineering/Applied Math

Tools

CUDA

ROCm

Metal

OpenACC

OpenMP

MPI

Pthreads

TBB

Job description

NVIDIA seeks a Senior DevTech Compute Engineer for Compression and Data Processing to prototype GPU-accelerated distributed data processing, including dataframe analytics and lossless/lossy compression methods. You will work with experts across industry and academia to optimize data-intensive workloads on GPU/CPU architectures.

The role involves collaboration with research, hardware, and software teams, influencing hardware/software co-design, and delivering solutions to top customers.