We are looking for an experienced and highly motivated software professional to work on pioneering initiatives and projects at the intersection of CUDA and Deep Learning Systems. As the complexity and scale of artificial intelligence continue to grow, the intersection of advanced deep learning architectures, massive-scale distributed computing, and low-level hardware optimization has never been more critical. Our team is dedicated to exploring and prototyping next-generation ideas that bridge the gap between deep learning algorithms and CUDA, pushing the boundaries of what is possible on modern accelerator architectures.
Join our dynamic, research-oriented team to help unlock maximum hardware performance for emerging AI workloads. You will be a crucial member of a highly technical group exploring uncharted territories in model optimization, custom kernel development, and cluster-scale AI systems design. If you are passionate about the fundamentals of deep learning and thrive on squeezing every ounce of performance out of advanced computing systems from a single GPU to supercomputer clusters, we want you on our team!
What you will be doing:
Explore, research, and prototype novel systems optimizations for advanced deep learning models at the intersection of high-level DL frameworks and low-level CUDA through modeling, simulation, and silicon prototyping.
Architect and optimize distributed computing systems that scale seamlessly from a single node to massive, cluster-scale supercomputing environments.
Design, implement, and optimize custom high-performance CUDA kernels tailored to emerging neural network architectures and workloads.
Analyze complex hardware-software interactions to identify and resolve performance bottlenecks in both training and inference pipelines.
Collaborate closely with AI researchers, HW and SW architects, kernel and compiler authors and CUDA driver experts to co-design systems and algorithms that improve accelerator compute utilization, memory bandwidth, cross-node network communication efficiency 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 degree achievement.
Strong proficiency in C++ and Python programming.
Solid background in the fundamentals of Deep Learning with a focus on transformers.
Strong understanding of distributed computing principles, multi-node scaling, and the unique performance challenges of cluster-scale execution.
Proven experience in systems programming, computer architecture, and low-level systems performance optimization.
Familiarity with deep learning accelerator architectures such as the GPU and hands-on experience with CUDA programming, kernel optimization, and workload profiling
Experience profiling and optimizing generative AI models, including but not limited to, pioneering large language models.
Research background in machine learning systems or adjacent fields and experience profiling and optimizing innovative vision models, generative AI architectures, or diffusion models.
A track-record of initiative and willingness to deep-dive on problems across the stack.
Ways to stand out from the crowd:
Deep expertise in performance internals and execution graphs of major deep learning training and inference frameworks (e.g., PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, Megatron).
Hands-on experience with communication libraries (e.g., NCCL, MPI, UCX) and distributed machine learning techniques (e.g., pipeline, tensor, expert parallelism).
Knowledge of numerical methods and low-precision arithmetic (e.g., NVFP4, MXFP4, FP8, INT8) and their impact on deep learning accuracy and performance.
Background in deep learning compilers and ML systems, including graph-level and codegen tools (e.g., Triton, XLA, torch.compile) and highly parallel/RL-style simulation environments.
Experience designing and implementing agentic AI systems applied to complex systems and infrastructure problems.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
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.Skills Required
- BS, MS, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related field (or equivalent experience).
- 8+ years of relevant industry experience or equivalent academic experience after degree achievement.
- Strong proficiency in C++ programming.
- Strong proficiency in Python programming.
- Solid background in the fundamentals of Deep Learning with focus on transformers.
- Strong understanding of distributed computing principles and multi-node scaling.
- Proven experience in systems programming, computer architecture, and low-level performance optimization.
- Familiarity with deep learning accelerator architectures such as GPUs and hands-on CUDA programming, kernel optimization, and workload profiling.
- Experience profiling and optimizing generative AI models (large language models and similar).
- Research background in machine learning systems or adjacent fields and experience profiling and optimizing novel vision/generative architectures.
- Demonstrated initiative and willingness to deep-dive on cross-stack problems.
- Deep expertise in performance internals and execution graphs of major DL frameworks (e.g., PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, Megatron).
- Hands-on experience with communication libraries and distributed ML techniques (e.g., NCCL, MPI, UCX; pipeline, tensor, expert parallelism).
- Knowledge of numerical methods and low-precision arithmetic (e.g., NVFP4, MXFP4, FP8, INT8) and their impact on accuracy/performance.
- Background in deep learning compilers and ML systems (e.g., Triton, XLA, torch.compile) and graph-level/codegen tools.
- Experience designing and implementing agentic AI systems applied to complex systems and infrastructure.
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
