NVIDIA

1 month ago

Senior Systems Software Engineer, AI Stack and Performance - DGX Station

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 17 hours agoStatus: Live

Job Summary

Deeply technical systems software engineer responsible for AI stack readiness on DGX Station. You will profile workloads, optimize GPU compute and interconnects, collaborate with framework/compiler teams, and ensure multi-GPU, multi-user workloads achieve best-in-class performance for real AI tasks.

Location: Santa Clara

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Own production readiness of AI applications on DGX Station—NemoClaw, Hermes agents, NIM microservices, and key customer workloads. Define “ready to ship” criteria, run validation, and close gaps between “it runs” and “it runs well” across single-GPU and multi-GPU configurations.
•Profile and optimize DL workloads (PyTorch, TensorFlow, JAX) across training and inference on the GB300 Blackwell multi-GPU architecture; establish benchmarks and identify regressions.
•Identify bottlenecks in GPU compute, NVLink bandwidth, memory, PCIe, and CPU–GPU communication; drive kernel tuning, memory placement, NVLink utilization, data pipelines, and scheduling improvements.
•Collaborate with framework, compiler (TensorRT, NVCC, Triton), and GPU architecture teams to improve kernel fusion, graph execution, and memory management for Blackwell GPUs.
•Validate multi-user and concurrent workloads, ensure DGX Station performs reliably as a shared workstation; maintain stack validation including CUDA toolkit, cuDNN, TensorRT, NCCL, Triton, DCGM, DOCA/OFED.

Pay and Benefits

Salary: USD 224,000 - 356,500

Equity and Bonus:Equity

Perks:EquityBenefits

Key Requirements

•BS or MS in Computer Science, Electrical Engineering, or related field.
•12+ years in systems software engineering with hands-on AI/ML workload optimization, GPU performance analysis, or deep learning infrastructure.
•Strong proficiency with deep learning frameworks—PyTorch, TensorFlow, or JAX—including internals: graph execution, operator dispatch, memory management, and custom kernel integration.
•Experience profiling and optimizing GPU workloads using Nsight Systems, Nsight Compute, CUPTI, or equivalent. Ability to read GPU traces and translate observations into actionable optimizations.
•Proficiency in C/C++, CUDA, and Python. Comfortable reading and modifying GPU kernels.

Experience:12+ yearsAIGPUDeep LearningLLM

Education:Master's

Skills:CUDAC++PythonPyTorchTensorFlowJAXNsightNCCLTensorRTTritonCuDNNCUDA toolkitDCGMGPGPU

Languages:English

Tech Stack:CUDAPythonC++PyTorchTensorFlowJAXNsightNCCLTensorRTTritonCuDNNDCGMDOCA/OFEDGPUNVLink

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

1 month ago

Senior Systems Software Engineer, AI Stack and Performance - DGX Station

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 17 hours agoStatus: Live

Job Summary

Location: Santa Clara

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Own production readiness of AI applications on DGX Station—NemoClaw, Hermes agents, NIM microservices, and key customer workloads. Define “ready to ship” criteria, run validation, and close gaps between “it runs” and “it runs well” across single-GPU and multi-GPU configurations.
•Profile and optimize DL workloads (PyTorch, TensorFlow, JAX) across training and inference on the GB300 Blackwell multi-GPU architecture; establish benchmarks and identify regressions.
•Identify bottlenecks in GPU compute, NVLink bandwidth, memory, PCIe, and CPU–GPU communication; drive kernel tuning, memory placement, NVLink utilization, data pipelines, and scheduling improvements.
•Collaborate with framework, compiler (TensorRT, NVCC, Triton), and GPU architecture teams to improve kernel fusion, graph execution, and memory management for Blackwell GPUs.
•Validate multi-user and concurrent workloads, ensure DGX Station performs reliably as a shared workstation; maintain stack validation including CUDA toolkit, cuDNN, TensorRT, NCCL, Triton, DCGM, DOCA/OFED.

Pay and Benefits

Salary: USD 224,000 - 356,500

Equity and Bonus:Equity

Perks:EquityBenefits

Key Requirements

•BS or MS in Computer Science, Electrical Engineering, or related field.
•12+ years in systems software engineering with hands-on AI/ML workload optimization, GPU performance analysis, or deep learning infrastructure.
•Strong proficiency with deep learning frameworks—PyTorch, TensorFlow, or JAX—including internals: graph execution, operator dispatch, memory management, and custom kernel integration.
•Experience profiling and optimizing GPU workloads using Nsight Systems, Nsight Compute, CUPTI, or equivalent. Ability to read GPU traces and translate observations into actionable optimizations.
•Proficiency in C/C++, CUDA, and Python. Comfortable reading and modifying GPU kernels.

Experience:12+ yearsAIGPUDeep LearningLLM

Education:Master's

Skills:CUDAC++PythonPyTorchTensorFlowJAXNsightNCCLTensorRTTritonCuDNNCUDA toolkitDCGMGPGPU

Languages:English

Tech Stack:CUDAPythonC++PyTorchTensorFlowJAXNsightNCCLTensorRTTritonCuDNNDCGMDOCA/OFEDGPUNVLink

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor