Senior Research Engineer - CUDA AI Quality, Senior Research Engineer - CUDA AI Quality

Senior Research Engineer - CUDA AI Quality

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NVIDIA's AI Developer Tools organization is seeking a Senior Research Engineer to join our Quality team, where we are 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.

Our growing team operates at the intersection of CUDA domain expertise and cutting‑edge AI research. While evaluation is our foundation—keeping the entire industry honest about AI’s capabilities in CUDA programming—we also work on diverse AI research initiatives including novel training methodologies, tool development for both human developers and AI agents, and dataset curation. For CUDA experts interested in AI research, we offer a unique opportunity to transition into this exciting field with extensive mentorship and hands‑on learning from a team that values both teaching and learning.

In this role, you’ll leverage your CUDA expertise across a variety of impactful projects. You’ll design evaluations that truly measure what matters in AI‑powered CUDA development, but you’ll also contribute to building AI developer tools, experimenting with novel training approaches, and creating the infrastructure that will enable the next generation of AI coding assistants. Your work will directly influence how NVIDIA and the broader ecosystem develop and evaluate AI tools for accelerated computing.

• 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 such as 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.

• B.S. in Computer Science or related technical field or equivalent experience (M.S. preferred).
• 1–2+ 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.

• 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…