Senior Software Engineer, DGX Cloud AI Infrastructure
$184k - $357k • Remote • US, CA, Santa Clara
Posted 20d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
AI Infrastructure Engineer
About the job
NVIDIA is seeking a Senior Software Engineer to lead the bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales. In this role, you will set technical direction for communication libraries, model frameworks, and inference/training stacks to ensure state-of-the-art LLM workloads run efficiently and reliably at scale. You will lead deep performance and reliability investigations on multi-GPU and multi-node deployments, define how new platforms are benchmarked and qualified, and build the resilience and failure-attribution capabilities essential for large clusters. This is a hands-on, senior individual-contributor role for an engineer experienced at the intersection of deep learning systems, GPU performance, distributed computing, and large-scale operations.
Responsibilities
- Lead the bring-up, validation, and debugging of large-scale AI clusters, infrastructure, and end-to-end workloads.
- Tune and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo/Megatron, TensorRT-LLM, and related NVIDIA AI software.
- Profile and optimize workload performance across compute, memory, networking, and communication layers using tools like Nsight Systems and NCCL tests.
- Analyze scaling efficiency for distributed LLM workloads across modern GPU clusters and provide tuning guidance.
- Perform root-cause analysis for complex failures in large distributed environments.
- Develop and implement resilience and failure-attribution systems for cluster-wide failure detection and attribution.
- Build repeatable benchmark suites, automation, acceptance criteria, and qualification workflows for new platforms.
- Tune runtime settings, communication parameters, and deployment configurations in collaboration with framework, systems, and platform teams.
- Deliver data-driven recommendations based on profiling, benchmark results, and cluster characterization.
- Mentor engineers, drive technical standards, and contribute to the broader performance and infrastructure organization.
Requirements
- Bachelor's or Master's in Computer Science or a related technical field (or equivalent experience).
- 8+ years of experience developing software infrastructure for large-scale AI or HPC systems, with a track record of technical leadership.
- Expertise in debugging and triaging AI applications across the full stack, from application layer to hardware.
- Deep hands-on experience with NCCL, CUDA-aware distributed execution, and debugging multi-GPU/multi-node workloads at scale.
- Proven track record of architecting, debugging, and scaling large-scale distributed systems.
- Expert-level Python and C/C++ programming skills.
- Experience operating workloads in scheduled, containerized cluster environments.
- Excellent analytical, debugging, and communication skills, with the ability to influence across teams.
Benefits
- Equity
- Benefits