Senior Deep Learning Software Engineer, Inference, Model Optimization

NVIDIA

. Train, develop, and deploy state-of-the-art generative AI models such as LLMs and diffusion models using NVIDIA's AI software stack .

Posted 10/9/2026full-timeRemote • California • United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing generative AI models, particularly using NVIDIA's AI software stack and the Torch ecosystem. Proficient in high-performance GPU kernel development and performance analysis, with a strong foundation in deep learning and machine learning frameworks.

Highest-signal resume keywords

Generative AI Model DevelopmentDeep Learning ExpertisePython ProficiencyGPU Kernel OptimizationNVIDIA TensorRT Familiarity

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Deep LearningGenerative AIPerformance AnalysisModel OptimizationPythonPyTorchCUDAAlgorithmsMachine Learning FrameworksHigh-Performance Computing

Soft Skills

CollaborationCommunicationIndependent WorkProblem-Solving

Tools & Technologies

NVIDIA AI Software StackTorch 2.0HuggingFaceTensorRTTRT-LLMTRT Model OptimizerTorchDynamoTorch.exportTorch.compileCUTLASS

Industry Keywords

GPU ArchitectureModel ShardingTensor ParallelismSequence ParallelismKV-CachingEnd-to-End PerformanceModular Software DesignScalable Software PlatformFast-Paced EnvironmentResearch Experience

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Train, develop, and deploy state-of-the-art generative AI models such as LLMs and diffusion models using NVIDIA's AI software stack
Leverage and build upon the Torch 2.0 ecosystem, including TorchDynamo, torch.export, and torch.compile, to analyze and extract standardized model graph representations from arbitrary Torch models
Develop high-performance inference optimization techniques, including automated model sharding, tensor parallelism, sequence parallelism, and efficient attention kernels with KV-caching
Collaborate with teams across NVIDIA to integrate performant kernel implementations into the automated deployment solution
Analyze and profile GPU kernel-level performance to identify hardware and software optimization opportunities
Continuously innovate inference performance for NVIDIA's TRT, TRT-LLM, and TRT Model Optimizer software solutions
Architect and design a modular, scalable software platform supporting broad model coverage and optimization techniques

Requirements

What you’ll need

Masters, PhD, or equivalent experience in Computer Science, AI, Applied Math, or related field
8+ years of relevant work or research experience in Deep Learning
Excellent software design skills, including debugging, performance analysis, and test design
Strong proficiency in Python, PyTorch, and related ML tools (e.g. HuggingFace)
Strong algorithms and programming fundamentals
Good written and verbal communication skills and the ability to work independently and collaboratively in a fast-paced environment
Contributions to PyTorch, JAX, or other Machine Learning Frameworks
Knowledge of GPU architecture and compilation stack, and capability of understanding and debugging end-to-end performance
Familiarity with NVIDIA's deep learning SDKs such as TensorRT
Prior experience in writing high-performance GPU kernels for machine learning workloads in frameworks such as CUDA, CUTLASS, or Triton

Benefits

Comp & perks

Highly competitive salaries
Comprehensive benefits package
Equity