NVIDIA
AI
SeniorFull-StackLeadEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Full-Stack Lead Engineer at NVIDIA. Skills: Full-stack depth, cloud expertise, containers and orchestration, CI/CD and safe deployment practices, API design, machine learning platforms. 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 ”
What You'll Achieve.
ensure NVIDIA’s AI infrastructure is used efficiently, transparently, and at scale; 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; meet exascale standards for reliability, performance, and observability; reduce complexity, support load and long-term tech debt; accelerate the work of AI researchers; improve code quality, testing, security, and observability; drive adoption of best practices within the team
Industry & Context.
problem-solving ability; GPU cluster debugging; performance triage; root-cause analysis
What They're Looking For.
Must Have
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), 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, orchestration (Docker, Kubernetes), mature CI/CD and safe deployment practices, Full-stack depth: modern SPA frameworks (React/Next. js or Vue/Nuxt), JavaScript/TypeScript, one or more backend languages (Node. js, Python, and/or Golang), Proficiency in API design (REST), schema evolution, integration patterns, 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, problem-solving ability, a growth mindset, Experience leveraging AI-assisted development tools (e. g. , Cursor)
Nice to Have
Hands-on ML platform depth (MLE experience or familiarity with DL frameworks such as PyTorch, TensorFlow, distributed training ecosystems like Ray), Datacenter-scale operational experience, including GPU cluster debugging, performance triage, and root-cause analysis across complex distributed systems
What You'll Do.
Lead the architecture and delivery of high-scale web products across frontend
with clear availability and latency targets (SLOs/SLAs)
Own multi-team initiatives end to end: problem discovery
and success metrics tied to product and business outcomes
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 improve code quality
and observability through reviews
Stay ahead of AI/ML infrastructure trends and drive adoption of best practices within the team
How You'll Work.
Team & Collaboration
cross-functional collaboration skills; Collaborate with NVIDIA AI Research teams
Communication Scope
Clear written and verbal communication skills; active listening
Process & Methodology
Own multi-team initiatives end to end: problem discovery, RFCs/design reviews, phased rollouts, and success metrics tied to product and business outcomes
Full Job Description
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. **What You’ll Be Doing:** * 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. **What We Need To See:** * 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 skill
Applying for this Senior Full-Stack Lead Engineer role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Workday
- Workday has a multi-step form — save your progress after every section.
- "Apply With LinkedIn" can fail or lose data; manual entry is more reliable.
- Watch for the "Submit for Review" final step — hitting "Save" alone does not submit.
- Job requisition numbers are useful when following up with HR by email.
ANONYMOUS · UNFILTERED
What do employees actually say about NVIDIA?
Real rants from real employees. Read before you apply.