2 days ago
Base Salary
$224k - $431k/yr
Responsibilities
- Improve reliability, performance, observability, release confidence, and end-user experience across desktop, web, and service-based AI products.
- Design and build resilient frontends, backend APIs, distributed services, data flows, and deployment systems for enterprise scale.
- Establish testing, debugging, CI/CD, safe rollout, auto-update, monitoring, incident response, and operational excellence practices.
- Build reusable agent capabilities including orchestration services, deep-agent workflows, memory and context services, evaluation frameworks, telemetry, and policy-aware tool integration.
- Validate and operationalize Nemotron, NVIDIA AI Blueprints, and related platform capabilities in enterprise production settings.
- Define architecture for secure agent discovery, collaboration, trust, and enterprise governance.
- Create shared architecture, components, documentation, and operational playbooks; mentor engineers and partner cross-functionally.
Requirements
- BS, MS, or equivalent experience in computer science or a related field.
- At least 12 years of experience building and operating production software systems, including significant architecture and delivery leadership across the full stack.
- Experience with enterprise application deployment, security, authentication, device management, and application lifecycle management.
- Experience building modern applications across frontend, backend, and platform layers using technologies such as TypeScript/JavaScript, React, Electron or similar desktop frameworks, Python, Go, and Java.
- Proven ability to take complex products from prototype to reliable, secure, well-operated production systems.
- Deep expertise in testing strategy, release engineering, observability, performance tuning, and incident response.
- Experience building shared services, internal platforms, SDKs, or core infrastructure used by multiple teams or products.
- Working knowledge of LLM-powered applications, RAG, tool use, CLI-based workflows, reusable skills, MCP-based integrations, evaluation loops, memory systems, and agentic workflows.
- Strong judgment, communication, cross-functional leadership, and ability to remain highly hands-on.
- Preferred experience includes hardening desktop or client applications, building common frameworks and developer tooling, creating reusable AI infrastructure, working with identity or agent collaboration systems, and using GPU-accelerated systems or NVIDIA AI technologies.
Benefits
- Eligible for equity and benefits.
- Base salary is listed for Level 5 and Level 6 and varies by location, experience, and comparable employee pay.
- Applications will be accepted at least until October 2, 2026.
- This posting is for an existing vacancy.
Tech Stack
Categories
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.