Software Engineer, DGX Cloud AI Infrastructure
$116k - $224k • Remote • US, CA, Santa Clara
Posted 4d 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 Software Engineer to focus on the bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at scale. This role involves debugging large-scale AI clusters, infrastructure, and end-to-end workloads, as well as designing and implementing benchmarking tooling, automation, and debugging workflows. It's a hands-on position for engineers passionate about deep technical challenges in deep learning systems, GPU performance, distributed computing, and large-scale operations.
Responsibilities
- Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads.
- Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and related NVIDIA AI software.
- Perform root-cause analysis of failures in large distributed environments.
- Contribute to resilience and failure-attribution tooling for detecting, triaging, and attributing node, fabric, and workload failures.
- Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms.
- Tune runtime settings, communication parameters, and deployment configurations in partnership with framework, systems, and platform teams.
- Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization.
Requirements
- Bachelor's or Master's in Computer Science or a related technical field (or equivalent experience).
- 3+ years of experience developing software for AI, HPC, or systems-level applications.
- Hands-on experience with multi-GPU or multi-node workloads and CUDA-aware distributed execution.
- Background in debugging and scaling distributed systems.
- Experience debugging and triaging AI applications across the full stack, from application level to hardware.
- Experience operating workloads in scheduled, containerized cluster environments.
- Excellent analytical, debugging, and communication skills, with a collaborative approach.
- Strong Python and C/C++ programming skills.
Benefits
- Equity
- Benefits