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Company

Original Job Post

NVIDIA · 3 days ago

Senior Deep Learning Systems Software Engineer - AI Infrastructure

United States

Full-time

Remote

Senior Level

$180K/yr - $339K/yr

8+ years exp

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Artificial Intelligence (AI)GPU

H1B Sponsorship

Growth Opportunities

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Responsibilities

Understand, analyze, profile, and optimize deep learning workloads on state-of-the-art hardware and software platforms.

Build tools to automate workload analysis, workload optimization, and other critical workflows.

Collaborate with cross-functional teams to analyze and optimize cloud application performance on diverse GPU architectures.

Identify bottlenecks and inefficiencies in application code and propose optimizations to enhance GPU utilization.

Drive end-to-end platform optimization from a hardware level to the application and service levels.

Design and implement performance benchmarks and testing methodologies to evaluate application performance.

Provide guidance and recommendations on optimizing cloud-native applications for speed, scalability, and resource efficiency.

Share knowledge and best practices with domain expert teams as they transition applications to distributed environments.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Masters in CSEECSEEApplication Performance EngineeringLarge Scale GPU InfrastructureDeep Learning Model ArchitecturesPytorchDistributed TrainingApplication Profiling ToolsNVIDIA NSightIntel VTuneComputer ArchitectureGPU ArchitectureNVIDIA InfrastructureSoftware StacksAnalyzing PerformanceModeling PerformanceTuning PerformancePythonC/C++Optimizing Application CodeAlgorithmsGPU ProgrammingCUDAOpenCLNVIDIA EcosystemPerformance OptimizationBenchmarkingDistributed SystemsHPC Storage

Required

Masters in CS, EE or CSEE or equivalent experience

8+ years of experience in application performance engineering

Experience using large scale multi node GPU infrastructure on premise or in CSPs

Background in deep learning model architectures and experience with Pytorch and large scale distributed training

Experience with application profiling tools such as NVIDIA NSight, Intel VTune etc.

Deep understanding of computer architecture, and familiarity with the fundamentals of GPU architecture. Experience with NVIDIA's Infrastructure and software stacks.

Proven experience analyzing, modeling and tuning DL application performance.

Proficiency in Python and C/C++ for analyzing and optimizing application code

Preferred

Strong fundamentals in algorithms and GPU programming experience (CUDA or OpenCL)

Understanding of NVIDIA's server and software ecosystem

Hands-on experience in performance optimization and benchmarking on large-scale distributed systems

Hands-on experience with NVIDIA GPUs, HPC storage, networking, and cloud computing.

In-depth understanding storage systems, Linux file systems, RDMA networking

Benefits

Equity

Company

NVIDIA

Glassdoor

4.6

NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI.

Founded in 1993

Santa Clara, California, USA

10,001+ employees

https://www.nvidia.com

H1B Sponsorship

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2020 (534)