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

NVIDIA•Santa Clara, CA

1d•$224,000 - $356,500

About The Position

DGX Station (Galaxy) is NVIDIA’s workstation-class AI computer—built on GB300 Blackwell GPUs with NVLink interconnect, delivering data-center-grade AI compute in a deskside form factor. DGX Station is shipped to OEM and OSV partners as a complete SW/FW GA release including firmware bundles, DGX BaseOS, GPU drivers, CUDA toolkit, DCGM, and DOCA/OFED. For DGX Station to deliver on its promise, AI applications like NemoClaw, LLM inference via NIM, Hermes agents, and deep learning frameworks must run production-ready out of the box—optimized for the multi-GPU, high-bandwidth architecture of this platform. We are looking for a deeply technical systems software engineer who will own AI stack readiness on DGX Station. You will profile workloads, identify bottlenecks across GPU compute, NVLink, memory, and host interconnects, drive optimizations across the full stack—from GPU kernels through frameworks to applications—and work hands-on with framework, compiler, and GPU architecture teams to ensure DGX Station delivers best-in-class performance for real AI workloads in multi-user and multi-GPU configurations.

Requirements

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.

Nice To Haves

Experience optimizing LLM training or inference on multi-GPU NVIDIA systems (DGX, HGX, or multi-GPU workstations).
Contributions to open-source AI frameworks, CUDA libraries, or inference engines.
Experience with multi-GPU communication optimization—NCCL tuning, NVLink utilization, collective operations, and parallel training strategies.
Track record of collaborating with compiler and hardware architecture teams to drive kernel fusion, graph optimization, or hardware-specific performance improvements.
Experience shipping AI-powered products where application performance on specific hardware was a hard shipping requirement.

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 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. Translate DGX Station’s platform-specific constraints and multi-GPU topology into actionable optimization requests for upstream teams.
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. Ensure DGX Station performs reliably as a shared workstation.
Validate the full NVIDIA AI software stack on DGX Station: CUDA toolkit, cuDNN, TensorRT, NCCL, Triton Inference Server, DCGM, and DOCA/OFED. Ensure version compatibility, functional correctness, and performance parity with reference data center configurations.
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. Make performance data visible and actionable for GA release decisions.
Work with product management and OEM/OSV partners to understand target use cases (local LLM training and inference, agentic AI, multi-user research, RTX Pro workloads) and ensure DGX Station delivers compelling performance for each. Support customer deployment readiness and field critical issues.