Job description

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. What you’ll be doing: • AI Application Readiness: 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. • DL Framework Performance: 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. • 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. What we need to see: • 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 . Ways to stand out from the crowd: • 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. NVIDIA is considered one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you! NVIDIA’s invention of the GPU in 1999 fueled the growth of PC gaming, redefined modern computer graphics, and revolutionized parallel computing. GPU deep learning has since ignited a new chapter in computing, powering AI systems that can perceive and interpret the world. Today, NVIDIA is recognized as the AI computing company, and we’re continuing to expand our teams with outstanding talent. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 31, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

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 .
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.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until August 31, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmlpython

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