Senior Full-Stack Lead Engineer

• 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.

• 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).

Ways to Stand Out from the Crowd

• 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.

At NVIDIA, you’ll be immersed in a diverse, supportive environment where you’re empowered to do your best work. The DGX Cloud AI Infrastructure team is at the core of NVIDIA’s AI efforts building the software that makes scalable research possible. Join us and help power the next wave of innovation. NVIDIA provides competitive salaries and a comprehensive benefits package. Our engineering teams are expanding rapidly due to exceptional growth.

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary range for this position is 224,000 USD – 356,500 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 30 years. Today, we're at the forefront of AI innovation powering breakthroughs in research, autonomous vehicles, robotics, and more. The DGX Cloud team builds and operates the AI infrastructure that fuels this progress.

We’re looking for a Senior Full-Stack Software Engineer to join the AI Hub team within the DGX Cloud AI Infrastructure organization. The AI Hub team accelerates AI research by ensuring NVIDIA’s AI infrastructure is used efficiently, transparently, and at scale. Our 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.