About the job
NVIDIA's AI Developer Tools organization is seeking a Senior Research Engineer to join our Quality team, where we're building the definitive benchmarks and evaluation frameworks for AI-powered CUDA programming, while also developing cutting-edge AI tools and methodologies for the future of accelerated computing. This role combines deep CUDA expertise with opportunities to work on exciting AI research projects that shape how artificial intelligence writes code for the world's most important parallel computing platform.
Responsibilities
Design and build evaluation frameworks to assess AI models' ability to generate, optimize, and maintain CUDA code across the full software development lifecycle
Develop benchmarks that represent real-world CUDA programming patterns and use cases across NVIDIA's ecosystem (kernels, libraries, multi-GPU applications)
Contribute to cutting-edge AI research projects including novel training methodologies, tool development, and dataset curation initiatives
Partner with teams developing CUDA-focused AI tools to provide evaluation insights, identify performance gaps, and integrate novel capabilities (e.g., RAG, profiling, web research)
Create and curate high-quality datasets, leveraging both synthetic generation and real-world CUDA code to advance the state of AI-powered programming
Explore and develop new AI tooling for developers, including IDE enhancements, cloud-served profiling services, and agent-ergonomic interfaces
Conduct experiments to validate new approaches in areas like reinforcement learning for code optimization and multimodal representation learning
Lead projects to expand our team's impact across different CUDA application domains and complexity levels
Qualifications
Minimum
B.S. in Computer Science or related technical field or equivalent experience (M.S. preferred)
12+ years of relevant technical experience, with at least 5 years of hands-on CUDA programming experience (kernel development, optimization, debugging)
Strong proficiency in Python and software engineering best practices
Experience shipping production code or tools (beyond purely academic research)
Experience with NVIDIA development tools (nvcc, CUDA toolkit)
Strong analytical and problem-solving skills with attention to detail
Ability to work independently while collaborating effectively across teams
Genuine interest in AI/ML and eagerness to learn new research methodologies
Preferred
Experience with ML/AI experimentation workflows and evaluation methodologies
Demonstrated ability to design rigorous benchmarks with attention to data quality and statistical validity, especially if those benchmarks have become industry standards
Experience building or evaluating code generation models or AI-powered development tools
Background with NVIDIA profiling and analysis tools (Nsight Compute, Nsight Systems) and/or the CUDA library ecosystem (cuDNN, cuBLAS, Thrust, CUB)
Experience with synthetic data generation and quality validation
Publications or open-source contributions in AI for code or CUDA optimization (with demonstrated real-world impact)
Experience working on highly-visible AI/ML products or foundation models