Senior Staff Engineer - Employee Productivity

NVIDIA•Santa Clara, CA

11d•$200,000 - $322,000

About The Position

NVIDIA is seeking an engineer to drive the evolution of AI-powered employee experiences across web and mobile, partnering with global teams. The role builds end-to-end features for seamless employee experiences and architects performant services and interfaces that deliver personalized content, knowledge, and search results to a global workforce. It incorporates LLM and vector-search capabilities into existing products, integrates third-party HRIS, collaboration, and productivity platforms, and applies modern front-end and cloud-native back-end practices. The engineer owns system design and code reviews, mentors team members, supports observability and reliability, and upholds security, privacy, accessibility, documentation, and knowledge-sharing standards.

Requirements

BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, another engineering field, or a related field, or equivalent experience.
12+ years of full-stack product engineering experience, including 3+ years at the principal or staff level guiding cross-functional teams.
Proven track record delivering consumer-grade web and/or mobile experiences at scale.
Expertise in search, recommendation, or applied-AI systems.
Hands-on experience with modern cloud infrastructure, Kubernetes, and automated CI/CD.
Strong data-modeling skills with relational and NoSQL stores.
Fluency in application security.
Strong mentoring skills, with the ability to provide vision and contribute directly to implementation.

Nice To Haves

Shipped generative-AI-powered features, such as RAG, copilots, or chatbots, to thousands of daily users.
Contributions to open-source search or front-end frameworks.

Responsibilities

Build end-to-end features for AI-rich employee experiences across web and native mobile applications.
Architect performant services and UIs that surface personalized content, knowledge, and search results to a global workforce.
Integrate LLM and vector-search capabilities into existing product surfaces and drive relevance and continuous learning loops.
Own system design and code reviews, raise engineering standards, and mentor engineers across the team.
Integrate third-party HRIS, collaboration, and productivity platforms to create cohesive, automated user journeys.
Promote modern front-end patterns and cloud-native back-end practices.
Instrument observability, including tracing, structured logging, and SLA dashboards, to support 24×7 reliability.
Uphold security, privacy, and accessibility standards across user journeys.
Promote technical documentation and knowledge-sharing sessions.