Senior Systems Software Engineer, AI Stack, Performance

NVIDIA

Systems Software Engineer optimizing AI stack performance on NVIDIA’s DGX Station. Leading efforts in AI application readiness and multi-GPU workload optimization.

Posted 6/1/2026full-timeSanta Clara • California • 🇺🇸 United StatesSenior💰 $224,000 - $356,500 per yearWebsite

Tech Stack

Tools & technologies

MicroservicesPythonPyTorchTensorflow

About the role

Key responsibilities & impact

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 every gap between “it runs” and “it runs well” across single-GPU and multi-GPU configurations.
Work cross functionally with different orgs to profile and optimize LLM and deep learning workloads (PyTorch, TensorFlow, JAX) across training and inference on the GB300 Blackwell multi-GPU architecture.
Characterize performance across model sizes, batch sizes, precision modes (FP16, INT8, FP8), and GPU scaling (single-GPU vs. multi-GPU with NVLink) to establish benchmarks and identify regression.
Identify bottlenecks in GPU compute, NVLink bandwidth, host memory, PCIe, and CPU–GPU communication.
Implement or drive optimizations across the stack: kernel tuning, memory placement, NVLink utilization, data pipeline efficiency, and scheduling to increase throughput on DGX Station’s multi-GPU topology.
Work with NVIDIA’s framework, compiler (TensorRT, NVCC, Triton), and GPU architecture teams to improve kernel fusion, graph execution, operator scheduling, and memory management for Blackwell GPUs.
Validate multi-user and concurrent workload scenarios—multiple users running simultaneous training jobs, inference serving alongside development, and resource isolation via MIG or time-slicing.
Validate the full NVIDIA AI software stack on DGX Station: CUDA toolkit, cuDNN, TensorRT, NCCL, Triton Inference Server, DCGM, and DOCA/OFED.
Build and maintain performance benchmarking infrastructure for DGX Station—automated regression tracking across key models (LLaMA, GPT, Stable Diffusion, Whisper), framework versions, and driver updates.

Requirements

What you’ll need

BS or MS or equivalent experience in Computer Science, Electrical Engineering, or related field.
12+ years in systems software engineering with hands-on experience in 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.
Strong understanding of GPU architecture: compute units, memory hierarchy, NVLink, multi-GPU scaling, and how they impact AI workload performance.
Experience with inference optimization: quantization (INT8/FP8), model compilation (TensorRT, torch.compile), batching strategies, and serving frameworks.
Proficiency in C/C++, CUDA, and Python.
Comfortable reading and modifying GPU kernels.

Benefits

Comp & perks

equity
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Hard Skills & Tools

AI applicationsGPU performance analysisdeep learning optimizationPyTorchTensorFlowJAXC/C++CUDAPythoninference optimization

Soft Skills

cross-functional collaborationproblem-solvinganalytical skillscommunicationactionable optimization