Senior Full-Stack Lead Engineer

NVIDIA•Santa Clara, CA

About The Position

The DGX Cloud team builds and operates the AI infrastructure that fuels NVIDIA's progress in AI innovation. The AI Hub team within this organization accelerates AI research by ensuring NVIDIA’s AI infrastructure is used efficiently, transparently, and at scale. The primary goal is to build a unified, self-service “single pane of glass” portal that enables AI researchers to efficiently manage, monitor, and optimize their use of Managed AI research Superclusters.

Requirements

12+ years of software engineering experience delivering production web systems.
Bachelor’s degree or higher in Computer Science or a related technical field (or equivalent experience).
Strong cross-functional collaboration skills, including active listening, translating complex use cases into clear technical requirements, and designing data models aligned with business logic and outcomes.
Deep cloud expertise (AWS, GCP, or Azure), infrastructure as code, containers, and orchestration (Docker, Kubernetes), along with mature CI/CD and safe deployment practices.
Full-stack depth: modern SPA frameworks (React/Next.js or Vue/Nuxt), JavaScript/TypeScript, and one or more backend languages (Node.js, Python, and/or Golang).
Familiarity with observability stacks such as OpenSearch, Prometheus, Grafana, or Loki.
Proficiency in API design (REST), schema evolution, and integration patterns, with a strong commitment to automated testing.
Experience building machine learning platforms or self-service internal infrastructure tools focused on efficiency, resiliency, and observability.
Clear written and verbal communication skills, strong problem-solving ability, and a growth mindset.
Experience leveraging AI-assisted development tools (e.g., Cursor).

Nice To Haves

Hands-on ML platform depth (MLE experience or strong familiarity with DL frameworks such as PyTorch, TensorFlow, JAX; distributed training ecosystems like Ray).
Datacenter-scale operational experience, including GPU cluster debugging, performance triage, and root-cause analysis across complex distributed systems.

Responsibilities

Lead the architecture and delivery of high-scale web products across frontend, backend services, and data layers, with clear availability and latency targets (SLOs/SLAs).
Own multi-team initiatives end to end: problem discovery, RFCs/design reviews, phased rollouts, and success metrics tied to product and business outcomes.
Drive reliability, performance, and observability improvements to meet exascale standards.
Establish engineering standards and reusable platforms/design systems to reduce complexity, support load and long-term tech debt.
Collaborate with NVIDIA AI Research teams to identify pain points and deliver the next generation user experience that accelerates their work.
Mentor and sponsor engineers; improve code quality, testing, security, and observability through reviews, pairing, and coaching.
Stay ahead of AI/ML infrastructure trends and drive adoption of best practices within the team.