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Senior Deep Learning Frameworks CUDA Software Engineer

NVIDIA· Engineer, Sys SW
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Hybrid (Santa Clara, CA), Hybrid (Austin, TX), Remote$184k - $287.5k + equityfull-timePosted 5mo ago

About the role

Deep Learning engineer focused on integrating CUDA features and distributed runtime technologies into AI stacks and frameworks, enabling scalable multi-GPU to microsecond-latency inference across large AI workloads.

Responsibilities

  • Integrate new CUDA features and Runtime abstractions in AI frameworks: from PoC to performance analysis to production
  • Perform deep analysis of AI workloads and frameworks to identify requirements and opportunities to innovate in the lower layers of the stack. Collaborate hands-on with teams working on the latest AI models.
  • Own and drive improvements in the AI Compiler-Runtime interface to build speed-of-light multi-GPU multi-node solutions.
  • Design fault-tolerant and elastic solutions for large-scale or dynamic AI workloads.
  • Influence the roadmap of core CUDA to facilitate building next-gen DL frameworks.
  • Collaborate with a very dynamic team across multiple time zones.
  • Collaborate closely with AI researchers, HW and SW architects, kernel and compiler authors and CUDA driver experts to co-design systems and frameworks that enhance performance and programmability.
  • Develop exploratory tools and runtime systems to profile and accelerate new paradigms in deep learning.
  • Write clean, effective, and maintainable code, ensuring exploratory prototypes can smoothly transition into open-source releases, upstream framework integrations, internal tools, or closed-source commercial products.

Qualifications

  • BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
  • 8+ years of relevant industry experience or equivalent academic experience after completed degree.
  • Development experience with Deep Learning Frameworks such PyTorch, JAX, and Inference Engines such as TRT-LLM, vLLM, SGLang
  • Rapid prototyping and development with Python, C++, CUDA or related DSLs
  • Solid grasp of AI models, parallelisms, and/or compiler technologies (e.g. torch.compile)
  • Experience conducting performance benchmarking on AI clusters. Familiarity with at least one performance profiler toolchain (PyTorch profiler, NVIDIA Nsight Systems)
  • Understanding of HPC/AI communication concepts
  • Good understanding of computer system architecture, HW-SW interactions and operating systems principles (aka systems software fundamentals)
  • Adaptability and passion to learn new frameworks and tools
  • Flexibility to work and communicate effectively across different teams and timezones

Skills

Required:CUDAPythonC++Torch/PyTorchJAXPerformance profilingHPC/AI communication

Benefits

  • equity
  • benefits
Last updated Sep 4, 2026. Interested? Apply on the company site.

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