Description

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

Key Skills

Education

Any Graduate