Senior Solutions Architect, Data Processing

Details

NVIDIA is currently seeking a Solutions Architect for High-Performance Databases. This role focuses on researching new algorithms and memory management techniques to accelerate databases on modern computer architectures and optimizing performance of data-intensive applications on GPUs and related systems.

Responsibilities

• Research and develop techniques to GPU-accelerate high performance databases, ETL and data analytics applications.
• Work directly with other technical experts (industry and academia) to perform in-depth analysis and optimization of complex data-intensive workloads to ensure the best possible performance on 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.

Requirements

• Masters or PhD in Computer Science, Computer Engineering, or a related computationally focused science degree, or equivalent experience.
• 8+ 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 (examples: CUDA (preferred), OpenACC, OpenMP, MPI, pthreads, TBB).
• In-depth expertise with CPU/GPU architecture fundamentals, especially memory subsystem.
• Domain expertise in high performance databases, ETL, data analytics and/or vector databases.
• Good communication and organization skills, with a logical approach to problem solving and prioritization.

Ways to stand out / Preferred

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

About the area / Additional context

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 ML and DL applications, since frameworks and core ML/DL libraries have been highly optimized for GPUs. Many applications have complex data analytics pipelines that can benefit from optimizations in memory management, compression, parallel algorithms (sort, search, join, aggregation, groupby), scaling up to multi-GPU systems, and scaling out to many nodes.

Open-source projects referenced: https://github.com/rapidsai/cudf/, https://github.com/NVIDIA/nvcomp, https://github.com/rapidsai/distributed-join, https://github.com/NVIDIA/cuCollections

Benefits

• You will be eligible for equity and benefits (see https://www.nvidia.com/en-us/benefits/).

Salary

• Base salary range for Level 4: 184,000 USD - 287,500 USD.
• Base salary range for Level 5: 224,000 USD - 356,500 USD.
• Final base salary will be determined based on location, experience, and pay of employees in similar positions.

Application deadline

Applications for this job will be accepted at least until October 26, 2025.

Equal opportunity

NVIDIA is committed to fostering a diverse work environment and is an equal opportunity employer. NVIDIA does not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.