1 day ago
Base Salary
$184k - $357k/yr
Responsibilities
- Build and deploy custom AI solutions, distributed training and inference workloads, and MLOps pipelines on NCP and Neo Cloud using NVIDIA reference architectures.
- Provide remote and on-site technical support to strategic NVIDIA Cloud Partners and guide partner engineering teams on NVIDIA platform practices.
- Deploy and manage AI workloads across DGX Cloud, NCP data centers, and major cloud service provider environments using Kubernetes, containers, and GPU scheduling systems.
- Profile and optimize large-scale training and inference workloads while implementing observability and SLO/SLA monitoring to reduce latency, cost, and operational risk.
- Develop integrations with partner control planes and customer environments, including API, data pipeline, and enterprise software connectivity.
- Create implementation guides, runbooks, and post-mortem documentation for operating NVIDIA AI workloads at scale.
Requirements
- Bachelor's, master's, or Ph.D. in computer science, computer/electrical engineering, or a related technical field, or equivalent experience.
- At least 8 years of experience in customer-facing technical roles such as Solutions Engineering, DevOps, Site Reliability, or ML Infrastructure Engineering.
- Strong expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling on multi-tenant or service-provider platforms.
- Production experience supporting large-scale AI/ML training and inference workloads, including LLMs, generative models, or recommendation systems.
- Strong programming skills in Python or Go and hands-on experience with PyTorch or TensorFlow for training and serving.
- Ability to collaborate with customer and partner engineering teams, conduct complex technical investigations, and drive issues to root cause and resolution.
- Strong communication and technical presentation skills for explaining architectures, trade-offs, and recommendations to engineering and leadership audiences.
- Preferred experience with NVIDIA DGX systems, CUDA, NeMo, Triton, NIM, InfiniBand, RoCE, NVIDIA Cloud Partners, hyperscale CSPs, MLOps, cloud-native practices, Prometheus, Grafana, OpenTelemetry, GitOps, Terraform, or Ansible.
Benefits
- Competitive base salary with eligibility for equity and benefits.
- Applications will be accepted at least until November 1, 2026.
- This posting is for an existing vacancy.
Categories
Solutions Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.