NVIDIA

Analytics and Data Intelligence

SeniorSoftwareEngineer,C++andCUDA

$184–357k Santa Clara, California, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Software Engineer, C++ and CUDA at NVIDIA. Skills: C++, CUDA, Parallel programming. Developing novel, parallel algorithms to accelerate core problems in data processing. Implementing solutions in C++ and CUDA”

What You'll Achieve.

Accelerate open-source software libraries for GPU data processing; Drive speed-of-light performance in structured data processing; Power the next generation of data center workflows; Be our next major contributor

Industry & Context.

Analytics and Data Intelligence
Problems you'll solve

Problem-solving skills; Apply your creativity to some of the most challenging and rewarding problems we have

What They're Looking For.

Must Have

5+ years of experience in Computer Science or Software Engineering, MS degree or PhD in computer science, engineering, or a related field (or equivalent experience), Modern C++ programming skills, Familiarity with at least one parallel programming framework, such as CUDA, OpenACC, OpenMP, etc., You care deeply about robust, readable, high-performance code, Excited to learn, explore new problem areas, and apply your creativity to some of the most challenging and rewarding problems we have

Nice to Have

Familiarity with RAPIDS cuDF, Experience in data science workflow development and debugging, Passion for publishing your work in technical blogs and conferences

What You'll Do.

parallel algorithms to accelerate core problems in data processing

Implementing solutions in C++ and CUDA

Contributing to open source projects

Working closely with experts in GPU hardware

software and workflows

Drive speed-of-light performance in structured data processing

Building the computational core for dataframe and database accelerators

How You'll Work.

Team & Collaboration

Working closely with the world’s top experts in GPU hardware, software and workflows

Full Job Description

We're looking for outstanding engineers and scientists to apply their parallel programming skills to accelerate open-source software libraries for GPU data processing. As part of NVIDIA’s Analytics and Data Intelligence (ADI) team, you will drive speed-of-light performance in structured data processing, spanning hardware from single workstations to multi-node GPU supercomputers. You will be building the computational core for dataframe and database accelerators—highly optimized C++ and CUDA libraries that leverage the parallel nature of GPUs to accelerate operations from data loading and parsing to joins, aggregations, and more. Come bring your creativity and problem-solving skills to our open-source software suite, and you can be our next major contributor! ****What you’ll be doing:**** * Developing novel, parallel algorithms to accelerate core problems in data processing and power the next generation of data center workflows * Implementing solutions in C++ and CUDA * Contributing to open source projects, such as cuDF, Velox, Presto and Spark * Benchmarking, profiling, and optimizing code * Working closely with the world’s top experts in GPU hardware, software and workflows ****What we need to see:**** * 5+ years of experience in Computer Science or Software Engineering * MS degree or PhD in computer science, engineering, or a related field (or equivalent experience) * Strong Modern C++ programming skills * Familiarity with at least one parallel programming framework, such as CUDA, OpenACC, OpenMP, etc. * You care deeply about robust, readable, high-performance code * Excited to learn, explore new problem areas, and apply your creativity to some of the most challenging and rewarding problems we have ****Ways to stand out from the crowd:**** * Familiarity with RAPIDS [_cuDF_](https://github.com/rapidsai/cudf) * Experience in data science workflow development and debugging * Passion for publishing your work in technical blogs and conferences. With competitive salarie

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