Senior Software Engineer, CUDA Deep Learning Systems

🔥 8 minutes ago

🏄 California, Texas – Remote

🏄 California – Remote 🤠 Texas – Remote

💵 $184k - $287.5k / year

⏰ Full Time

🟠 Senior

⚙️ Systems Engineer

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Explore, research, and prototype novel systems optimizations for advanced deep learning models at the intersection of high-level deep learning frameworks and low-level CUDA through modeling, simulation, and silicon prototyping • Architect and optimize distributed computing systems scaling from a single node to massive, cluster-scale supercomputing environments • Design, implement, and optimize custom high-performance CUDA kernels for emerging neural network architectures and workloads • Analyze hardware-software interactions to identify and resolve performance bottlenecks in training and inference pipelines • Collaborate with AI researchers, hardware and software architects, kernel and compiler authors, and CUDA driver experts to co-design systems and algorithms • Develop exploratory tools and runtime systems to profile and accelerate new deep learning paradigms • Write clean, effective, maintainable code and transition exploratory prototypes into open-source releases, framework integrations, internal tools, or commercial 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 degree achievement • Strong proficiency in C++ and Python programming • Solid fundamentals of deep learning, focused on transformers • Strong understanding of distributed computing, multi-node scaling, and cluster-scale execution performance • Proven systems programming, computer architecture, and low-level systems performance optimization experience • Familiarity with GPU deep learning accelerator architectures • Hands-on CUDA programming and kernel optimization experience • Experience using profiling tools to understand software performance on hardware • Experience profiling and optimizing vision models, generative AI architectures, or diffusion models • Background in deep learning compilers, including graph-level and codegen tools such as Triton, XLA, and torch compile • Deep learning framework internals and execution graph expertise is a differentiator • Hands-on CUDA, NCCL, MPI, UCX, and distributed machine learning experience is a differentiator • Knowledge of numerical methods and low-precision arithmetic such as NVFP4, MXFP4, FP8, and INT8 is a differentiator • Familiarity with reinforcement learning or highly parallel simulation environments is a differentiator • Machine learning experience, especially agentic systems applied to systems problems, is a differentiator

🏖️ Benefits

• Equity • Benefits