[Remote] Senior Solutions Architect, Data Processing at NVIDIA

California, United States

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Not SpecifiedCompensation

N/AExperience Level

N/AJob Type

Not SpecifiedVisa

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Requirements

Master's or PhD in Computer Science, Computer Engineering, or related computationally focused science degree or equivalent experience
8+ years of experience
Programming fluency in C/C++
Hands-on experience with low-level parallel programming (e.g. CUDA, 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
Logical approach to problem solving, and prioritization skills
Experience optimizing/implementing database operators or query planner, especially for parallel or distributed frameworks
Background with optimizing vector database index build and/or search
Experience profiling and optimizing CUDA kernels
Knowledge of compression, storage systems, networking, and distributed computer architectures
Experience with data analytics pipelines and optimizations in memory management, compression, parallel algorithms, and scaling up to multi-GPU systems

Responsibilities

Research and develop techniques to GPU-accelerate high performance database, ETL, and data analytics applications
Investigate hardware and system bottlenecks, and optimize performance of data-intensive applications
Influence the design of next-generation hardware architectures, software, and programming models in collaboration with research, hardware, system software, libraries, and tools teams
Influence partners (industry and academia) to push the bounds of data processing with NVIDIA's full product line
Work directly with other technical experts in their fields to perform in-depth analysis and optimization of complex data-intensive workloads
Collaborate with research, hardware, system software, libraries, and tools teams to design and implement next-generation hardware architectures, software, and programming models
Develop and implement optimized database operators, query planners, and parallel algorithms for high-performance data-intensive applications
Profiling and optimizing CUDA kernels for high-performance data-intensive applications
Knowledge of data preprocessing and data engineering, and experience with compression, storage systems, networking, and distributed computer architectures
Experience with data analytics and machine learning (ML) and deep learning (DL) applications, and knowledge of optimizing vector database index build and/or search

Skills

NVIDIA

Designs GPUs and AI computing solutions

About NVIDIA

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products include GPUs tailored for gaming and professional use, as well as platforms for artificial intelligence (AI) and high-performance computing (HPC) that cater to developers, data scientists, and IT administrators. NVIDIA generates revenue through the sale of hardware, software solutions, and cloud-based services, such as NVIDIA CloudXR and NGC, which enhance experiences in AI, machine learning, and computer vision. What sets NVIDIA apart from competitors is its strong focus on research and development, allowing it to maintain a leadership position in a competitive market. The company's goal is to drive innovation and provide advanced solutions that meet the needs of a diverse clientele, including gamers, researchers, and enterprises.

Santa Clara, CaliforniaHeadquarters

1993Year Founded

$19.5MTotal Funding

IPOCompany Stage

Automotive & Transportation, Enterprise Software, AI & Machine Learning, GamingIndustries

10,001+Employees

Benefits

Company Equity

401(k) Company Match

Risks

Increased competition from AI startups like xAI could challenge NVIDIA's market position.

Serve Robotics' expansion may divert resources from NVIDIA's core GPU and AI businesses.

Integration of VinBrain may pose challenges and distract from NVIDIA's primary operations.

Differentiation

NVIDIA leads in AI and HPC solutions with cutting-edge GPU technology.

The company excels in diverse markets, including gaming, data centers, and autonomous vehicles.

NVIDIA's cloud services, like CloudXR, offer scalable solutions for AI and machine learning.

Upsides

Acquisition of VinBrain enhances NVIDIA's AI capabilities in the healthcare sector.

Investment in Nebius Group boosts NVIDIA's AI infrastructure and cloud platform offerings.

Serve Robotics' expansion, backed by NVIDIA, highlights growth in autonomous delivery services.

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