Software Engineer, CUDA-Q

NVIDIA AI; California (MO); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Equity

Job summary

NVIDIA AI is seeking a senior software engineer to develop the CUDA-Q platform, building core infrastructure for inter-device communication and efficient execution across multi-processor systems.

You will collaborate with architects and product managers to create an extensible toolchain integrating quantum architecture components, performance profiling, and compiler fundamentals. This role demands a strong background in large-scale software and HPC skills.

Qualifications

• Requires a Bachelor's degree in Computer Science, Physics, or a related field with 5+ years of experience in large-scale software projects.
• Strong understanding of performance profiling, multi-processor systems, and compiler fundamentals.

Responsibilities

• Develop the CUDA-Q platform by building core infrastructure for inter-device communication.
• Collaborate with architects and product managers to create an extensible toolchain.
• Integrate quantum architecture components and optimize execution across multi-processor systems.

Skills

Distributed Systems

Performance Profiling

Multi-processor Systems

Compiler Fundamentals

CUDA-Q

GPU Programming

Quantum Computing Hardware

Quantum Error Correction

Inter-device Communication

Code Generation

Software Engineering

Education

Bachelor's degree in Computer Science, Physics, or related field

Tools

CUDA-Q

LLVM

MLIR

Job description

Develop the CUDA-Q platform by building core infrastructure for inter-device communication and efficient execution across multi-processor systems. Collaborate with architects and product managers to create an extensible toolchain integrating quantum architecture components.

Requirements

Requires a Bachelor's degree in Computer Science, Physics, or a related field with 5+ years of experience in large-scale software projects. Candidates must have a strong understanding of performance profiling, multi-processor systems, and compiler fundamentals.

Key Skills

• CUDA-Q
• Distributed Systems
• Compiler Fundamentals
• Performance Profiling
• LLVM
• MLIR
• GPU Programming
• FPGA Programming
• Quantum Computing Hardware
• Quantum Error Correction
• HPC
• System Design
• Inter-device Communication
• Code Generation
• Software Engineering
• Multi-processor Systems

Benefits

• Equity