Senior Software Engineer, CUDA Deep Learning Systems

NVIDIA

Senior software engineer researching and optimizing CUDA kernels, distributed systems, and deep-learning models at NVIDIA. Building high-performance AI infrastructure from single GPUs to supercomputer clusters.

Posted 8/5/2026full-timeRemote • California, Texas • 🇺🇸 United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in deep learning model optimization, CUDA programming, and distributed computing systems. Proficient in analyzing hardware-software interactions and developing high-performance solutions for advanced AI applications.

Highest-signal resume keywords

C++ ProgrammingPython ProgrammingCUDA ProgrammingDeep Learning FundamentalsDistributed Computing

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

CUDA Kernel OptimizationPerformance Bottleneck AnalysisSystems ProgrammingComputer ArchitectureWorkload ProfilingGenerative AI Model OptimizationTransformersMulti-Node ScalingLow-Level Systems Performance OptimizationDeep Learning Compilers

Soft Skills

CollaborationInitiativeProblem-Solving

Tools & Technologies

PyTorchJAXTensorRTNCCLMPIUCXTritonXLATorch.compileSgLang

Industry Keywords

Deep LearningAI SystemsNeural Network ArchitecturesCluster-Scale PerformanceGenerative AI ArchitecturesVision ModelsDiffusion ModelsNumerical MethodsLow-Precision ArithmeticAgentic AI Systems

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 through modeling, simulation, and silicon prototyping
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
8+ 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, with a focus 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 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 vision models, generative AI architectures, or diffusion models
Track record of initiative and willingness to deep-dive on problems across the stack
Preferred: expertise in performance internals and execution graphs of PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, or Megatron
Preferred: experience with NCCL, MPI, UCX, and distributed machine learning techniques such as pipeline, tensor, or expert parallelism
Preferred: knowledge of numerical methods and low-precision arithmetic such as NVFP4, MXFP4, FP8, or INT8
Preferred: background in deep learning compilers and ML systems, including Triton, XLA, or torch.compile
Preferred: experience designing agentic AI systems for complex systems and infrastructure problems

Benefits

Comp & perks

Equity
Benefits
Equal opportunity employer
Inclusive work environment