Hardware Engineer - System Features and System Integration

NVIDIA's Silicon Co-Design Group is seeking a flexible hardware engineer to join its System Features and System Integration teams. The role involves feature integration, debugging, validation, and productization for NVIDIA GPU, CPU, Client, and SoC products.

Key Responsibilities:

• End-to-end responsibility for integrating and developing system features across boot, clocks, DVFS, performance and power system controllers, and certain circuits.
• Lead debugging and root-cause resolution on complex cross-stack feature issues involving silicon, firmware, driver, and platform.
• Develop and deploy AI-enabled workflows for feature validation, debug, and triage.
• Support silicon bring-up, feature deployment, and GPU/SoC qualification.
• Build tools, scripts, and infrastructure for the team and improve validation and productization processes.
• Collaborate with globally distributed teams, including architecture, ASIC, firmware, driver, software, and multiple bring-up teams.

Requirements:

• BTech/BE or MTech/ME in Electronics, Electrical, or Computer Engineering with over 5 years of experience in post-silicon bring-up, system integration, or feature design and validation.
• Strong understanding of hardware, firmware, and software interaction, with fundamentals in board and system development, DVFS, control loops, SI/PI, timing, clocking, high-speed I/O, PVT, and thermal.
• Practical understanding of GPU, CPU, or SoC architectures within PC, datacenter, or automotive environments.
• Proven validation approach using smart AI technology with measurable impact on debug velocity, coverage, or escape rate.

Nice to Have:

• Experience building reusable feature-integration methodology, debug playbooks, or validation frameworks.
• Practical subsystem expertise in DVFS, boot/clock/reset, power management controllers, high-speed I/O, or thermal.
• Experience partnering with counterpart teams in other major engineering hubs.
• AI work that goes beyond personal-copilot uses, such as agentic workflows, RAG-grounded debug assistants, machine learning-based anomaly detection, or automated triage.