Develop shift-left solutions that embed security standards directly into AI-generated code, ensuring constraints, scaffolds, rules, and guardrails are applied during the coding process rather than post-shipment.
Define and maintain the performance data requirements from various sources including profiling libraries, annotation APIs, frameworks, runtimes, communication libraries, schedulers, configurations, and infrastructure telemetry.
Design and implement agentic AI workflows using LLM-based agents, tool calling, RAG patterns, and orchestration frameworks. Push the boundaries of what AI-assisted operations can achieve.
Work with development teams on test plans for all layers of SW stack for cloud infrastructure, execution, reviews, failure analysis and assessing overall quality and risk. Work with customer PMs on software issues including technical feedback from OEMs and CSPs.
Global M&A Payroll Integration. Lead acquisition payroll integrations across regions, defining scope, achievements, responsibilities, dependencies, and readiness criteria.
Own day-to-day site operations — incidents, requests, critical issues, and support coverage — with accountability for queue health, SLA attainment, backlog, and service quality, plus site asset and inventory management across lifecycle, refresh, procurement, and compliance.
Own day-to-day site operations — incidents, requests, critical issues, and support coverage — with accountability for queue health, SLA attainment, backlog, and service quality, plus site asset and inventory management across lifecycle, refresh, procurement, and compliance.
Participate in cutting edge Processor design in deep submicron technologies. Work as part of a global circuits team to design innovative circuits (digital, semi-custom and mixed-signal analog) for silicon correlation and noise mitigation.
Help create and deploy NVIDIA’s reference AI Super Computer datacenter designs at ODM partners. Collaborate with internal server AI Product design teams on next generation projects and product roadmaps.
Understand and analyze the efficiency, scaling, and resilience challenges in ML systems, algorithms, and applications. Develop creative systems solutions (hardware, software, infrastructure) for future ML systems of all scales.
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