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

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 Nemo Claw, 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.

• AI Application Readiness:
  Own production readiness of AI applications on DGX Station—Nemo Claw, 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.
• DL Framework Performance:
  Work cross functionally with different orgs to profile and optimize LLM and deep learning workloads (PyTorch, Tensor Flow, 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.
• System-Level Optimization:
  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.
• Compiler & Kernel
  
  Collaboration:
  
  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.
• Multi-User & Concurrency:
  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.
• Stack Validation:
  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.
• Benchmarking & Regression:
  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.
• Customer & Partner Alignment:
  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.

• 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, Tensor Flow, or JAX—including internals: graph execution, operator…