Director of GPU-Accelerated Data Analytics

NVIDIA Corporation; Santa Clara (CA); Hybrid; USD 224,000 - 431,000; Full time

Benefits offered by this job

Equity

Benefits

Hybrid work model

Job summary

NVIDIA Corporation seeks a Manager of GPU Accelerated Data Analytics to lead a team focused on performance engineering for CPU/GPU workloads. You will drive innovations, mentor engineers, and partner with architecture and research teams to deliver high-impact optimizations for data analytics across cloud and on-prem environments.

The role requires advanced degrees, extensive CUDA/C/C++ expertise, and a track record of leading high-performance teams.

Qualifications

• MS or PhD in Computer Science, Computer Engineering, or related field.
• 7+ years of total experience with 4+ years in a technical role and 3+ years in engineering leadership.
• Strong leadership, collaboration, and project execution skills.
• Hands-on experience in low-level performance optimization and GPU programming with CUDA.
• Proficiency in C/C++ and deep understanding of algorithms and software development.

Responsibilities

• Drive innovation by researching and developing techniques to optimize performance of complex workloads across cloud and on-prem environments.
• Provide technical leadership in software design decisions and influence architecture roadmap.
• Grow and mentor a distributed team of performance engineers and foster a collaborative culture.
• Collaborate with leadership, research teams, and cross-functional partners to advance initiatives.

Skills

CUDA programming

C/C++

GPU architecture

Algorithms

Performance optimization

Education

MS or PhD in Computer Science/Engineering or related field

Job description

NVIDIA Corporation seeks a Manager of GPU Accelerated Data Analytics to lead a team focused on performance engineering for CPU/GPU workloads. You will drive innovations, mentor engineers, and partner with architecture and research teams to deliver high-impact optimizations for data analytics across cloud and on-prem environments.

The role requires advanced degrees, extensive CUDA/C/C++ expertise, and a track record of leading high-performance teams.