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About the Role
NVIDIA is seeking a motivated Deep Learning engineer to integrate advanced CUDA features and Distributed Runtime technologies into AI stacks like PyTorch, TRT-LLM, vLLM, SGLang, and JAX. You will work with the team that created core CUDA features and runtimes, focusing on scaling Deep Learning and HPC applications for diverse multi-GPU demands, from large-scale training to microsecond latency inference. Your work will accelerate AI applications for the community and advance the state of the art in this space.
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 for innovation 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 for 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 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.
- Develop exploratory tools and runtime systems to profile and accelerate new paradigms in deep learning.
- Write clean, effective, and maintainable code for smooth transitions of prototypes into releases, integrations, or products.
Requirements
- 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.
- Adaptability and passion to learn new frameworks and tools.
- Flexibility to work and communicate effectively across different teams and timezones.
Skills
- 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 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.
Experience Level
- Level 4
- Level 5
Education Level
- BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
Salary/Compensations
- 184,000 USD – 287,500 USD for Level 4
- 224,000 USD – 356,500 USD for Level 5
Benefits
- Equity
- Benefits
About the Company
- 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.
Equal Opportunity
- NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer.
- As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) 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.