Senior Deep Learning Frameworks CUDA Software Engineer

NVIDIA; Town of Texas (WI); On-site; USD 184,000 - 356,500; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA is seeking a highly skilled individual for AI development focused on CUDA integration and performance optimization. Candidates should possess a BS, MS, or PhD in relevant fields, with 8+ years of experience in deep learning frameworks and CUDA.

This role involves working closely with other experts to innovatively improve AI workloads and systems. The base salary ranges from 184,000 USD to 356,500 USD, depending on level and experience. Inclusion and diversity are core to NVIDIA's values.

Qualifications

• 8+ years of relevant industry experience or equivalent academic experience after completed degree.
• Development experience with Deep Learning Frameworks such as PyTorch, JAX.
• Solid grasp of compiler technologies (e.g., torch.compile).

Responsibilities

• Integrate new CUDA features and Runtime abstractions in AI frameworks.
• Perform deep analysis of AI workloads to identify opportunities for innovation.
• Develop exploratory tools and runtime systems to profile and accelerate deep learning.

Skills

Deep Learning Frameworks

CUDA

Python

C++

AI models understanding

Education

BS, MS, or PhD in Computer Science or related field

Tools

Performance profiler tools

CUDA

Job description

NVIDIA is leading the way in groundbreaking developments in Artificial Intelligence, High Performance Computing and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions from artificial intelligence to autonomous cars.

What You Will Be Doing

• 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.

What We Need To See

• 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 as 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

Ways To Stand Out From The Crowd

• Deep expertise in the performance internals and execution graphs of major deep learning autograd, training and inference frameworks (e.g., PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, Megatron, MaxText, etc.).
• Hands‑on experience with CUDA, specific communication libraries (e.g., NCCL, MPI, UCX) and distributed machine learning techniques (e.g., pipeline parallelism, tensor parallelism).
• Expertise in one or more of these areas: Training, Distributed inference, MoE, Reinforcement Learning, kernel authoring (on CUDA, Triton, cuTe, etc).
• Background in deep learning compilers, both graph‑level and codegen (e.g., Triton, XLA, torch compile)
• Experience with programming for compute & communication overlap in distributed runtime

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until July 1, 2026.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.