Top Tech Jobs & Startup Jobs

Manage day-to-day silicon operations programs including NPI supply, scheduling, risk and dependency tracking, cross-functional coordination with engineering, foundry, supply chain and OSATs, reporting, process improvement, and driving issue closure and accountability to ensure chip supply and operational readiness.

Lead strategy and execution for production silicon diagnostics (ATPG, memory), building scalable diagnostics platforms, automation, and multi-functional operating models to accelerate yield learning, failure analysis, and product ramp for advanced-node technologies.

Lead and manage a software engineering team building and operating the NVIDIA Cloud Functions platform for GPU-accelerated workloads. Oversee project planning, scheduling, and delivery; prioritize scope; ensure reliability, scalability, and integration across deployment environments; collaborate with product, security, SRE, GPU infrastructure, and architecture; and foster an inclusive, high-performing team.

Develop and deliver verification tools for GPU kernels, integrating AI into workflows and participating in architectural discussions.

Lead research and architectural direction for AI systems communication at scale, conducting original research, optimizing hardware-software performance, and mentoring engineers.

The AI Compiler Engineer will innovate in AI performance, optimize compiler processes, and collaborate on hardware/software co-design, advancing AI technologies for datacenter applications.

The role involves creating technical marketing content for EDA technologies, collaborating with teams, benchmarking performance, and developing training materials.

Drive fleet management solutions for AI infrastructure, focusing on architecture, health monitoring, product life cycles, and collaboration across teams.

The Implementation Methodology Engineer will oversee design methodologies, optimize implementation flows, and collaborate with design teams to improve performance in complex projects.

Design and build RL post-training infrastructure for AI, optimizing training-inference-rollout on distributed systems while ensuring fault tolerance and scalability.