Software Engineer, CUDA Deep Learning Systems

NVIDIA

NVIDIA software engineer researching CUDA kernels and distributed systems for advanced deep-learning workloads. Optimizing GPU performance across training, inference, and cluster-scale AI infrastructure.

Posted 8/5/2026full-timeSanta Clara • California, Texas • 🇺🇸 United StatesJuniorMid-Level💰 $124,000 - $195,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in CUDA programming, high-performance computing, and deep learning optimization, with a strong foundation in distributed systems and machine learning architectures. Capable of collaborating with cross-functional teams to design and implement innovative solutions for advanced AI models.

Highest-signal resume keywords

CUDA ProgrammingC++ and Python ProficiencyDeep Learning OptimizationDistributed Computing SystemsPerformance Bottleneck Analysis

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

CUDAC++PythonDeep Learning FundamentalsSystems ProgrammingComputer ArchitectureKernel OptimizationWorkload ProfilingGenerative AI ModelsTransformers

Soft Skills

CollaborationInitiativeProblem-Solving

Tools & Technologies

PyTorchJAXTensorRTNCCLMPIUCXTritonXLATorch.compileSgLang

Industry Keywords

Machine Learning SystemsHigh-Performance ComputingAI ResearchNeural Network ArchitecturesCluster-Scale Performance

Tech Stack

Tools & technologies

Node.jsPythonPyTorch

About the role

Key responsibilities & impact

Explore, research, and prototype systems optimizations for advanced deep learning models at the intersection of high-level deep learning frameworks and low-level CUDA.
Architect and optimize distributed computing systems from single-node to 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, and maintainable code and transition prototypes into open-source releases, framework integrations, internal tools, or commercial products.

Requirements

What you’ll need

BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field, or equivalent experience.
2+ years of relevant industry experience or equivalent academic experience after degree achievement.
Strong proficiency in C++ and Python programming.
Solid background in deep learning fundamentals, focused on transformers.
Strong understanding of distributed computing, multi-node scaling, and cluster-scale performance challenges.
Proven experience in systems programming, computer architecture, and low-level systems performance optimization.
Familiarity with GPU deep learning accelerator architectures.
Hands-on experience with CUDA programming, kernel optimization, and workload profiling.
Experience profiling and optimizing generative AI models, including large language models.
Research background in machine learning systems or adjacent fields.
Experience profiling and optimizing innovative vision models, generative AI architectures, or diffusion models.
Track record of initiative and willingness to deep-dive on problems across the stack.
Preferred experience with PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, or Megatron internals and execution graphs.
Preferred hands-on experience with NCCL, MPI, or UCX and distributed machine learning techniques.
Preferred knowledge of numerical methods and low-precision arithmetic such as NVFP4, MXFP4, FP8, and INT8.
Preferred background in deep learning compilers and ML systems, including Triton, XLA, and torch.compile.
Preferred experience with highly parallel or reinforcement-learning-style simulation environments.
Preferred experience designing and implementing agentic AI systems for complex systems and infrastructure problems.

Benefits

Comp & perks

Equity
Benefits
Equal opportunity employer
Inclusive work environment