Solution Architect for High-Performance Databases

• In this role, you will research and develop techniques to GPU-accelerate high performance database, ETL and data analytics applications.

• Work directly with other technical experts in their fields (industry and academia) to perform in-depth analysis and optimization of complex data intensive workloads to ensure the best possible performance of current GPU architectures.

• Influence the design of next-generation hardware architectures, software, and programming models in collaboration with research, hardware, system software, libraries, and tools teams at NVIDIA

• Influence partners (industry and academia) to push the bounds of data processing with NVIDIA’s full product line

• Masters or PhD in Computer Science, Computer Engineering, or related computationally focused science degree or equivalent experience.

• 5+ years of experience.

• Programming fluency in C/C++ with a deep understanding of algorithms and software design.

• Hands-on experience with low-level parallel programming, e.g. CUDA (preferred), OpenACC, OpenMP, MPI, pthreads, TBB, etc.

• In-depth expertise with CPU/GPU architecture fundamentals, especially memory subsystem.

• Domain expertise in high performance databases, ETL, data analytics and/or vector database.

• Good communication and organization skills, with a logical approach to problem solving, and prioritization skills.

• Experience optimizing/implementing database operators or query planner, especially for parallel or distributed frameworks (e.g. production database or Spark).

• Background with optimizing vector database index build and/or search.

• Experience profiling and optimizing CUDA kernels.

• Background with compression, storage systems, networking, and distributed computer architectures.

Data Analytics is one of the rapidly growing fields in GPU accelerated computing. Data preprocessing and data engineering are traditionally CPU based and are becoming the bottleneck for Machine Learning (ML) and Deep Learning (DL) applications, as performance of the frameworks and core ML/DL libraries has been highly optimized leveraging GPUs. Many of today’s applications have complex data analytics pipelines that can benefit from optimizations in memory management, compression, parallel algorithms like sort, search, join, aggregation, groupby, scaling up to multi GPU systems, and scaling out to many nodes. Take a look at some of the open-source projects that NVIDIA employees have worked on: RAPIDS cuDF , NVIDIA nvcomp , NVIDIA Distributed join , NVIDIA cuCollections . NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and dedicated people in the world working for us. If you're creative and autonomous, we want to hear from you.

#deeplearning