Description
Join NVIDIA, a trailblazer at the forefront of graphics and artificial intelligence performance, efficiency, and innovation. The Silicon Codesign Group (SCG) sits at the intersection of architecture, silicon, systems, and manufacturing,where deep engineering judgment drives real-world product performance at scale.
The SCG Architecture team is hiring a Senior System–Manufacturing Co-Design Engineer to design systems and silicon features that optimize power, performance, thermal efficiency, and manufacturability across GPUs and SoCs. This role bridges architecture and manufacturing, translating product intent into scalable, testable performance while minimizing inefficiencies and cost.
Key Responsibilities:
- Architect system and silicon features to enhance performance, power efficiency, and thermal behavior.
- Drive improvements in V/F curves, Vmin, TGP, speed grading, and thermal envelopes through co-design.
- Design chip, system, and package-aware features that enhance testability, coverage, and yield.
- Define manufacturing-aware methodologies linking test, SRAM behavior, binning, and package constraints to product performance.
- Co-design test strategies and screening methods to reduce overkill, test time, and miscorrelation.
- Operate across the full lifecycle,from system architecture and pre-silicon strategy to post-silicon success.
- Translate ambiguous product requirements into executable architectures and validation plans.
- Leverage AI tools to accelerate engineering work while applying strong judgment on when to trust, verify, or override outputs.
Requirements:
- Master’s degree (or equivalent experience) in Electrical Engineering, Computer Engineering, Computer Science, Systems Engineering, or related field.
- 8+ years of experience in system architecture, silicon performance, manufacturing co‑design, or post‑silicon validation.
- Deep understanding of DVFS, binning, power/thermal management, and performance trade-offs in advanced GPUs or SoCs.
- Ability to reason across circuit behavior, system constraints, and manufacturing realities.
- Comfort with hands-on lab work as well as abstract architectural reasoning.
- Strong scripting and analysis skills (e.g., Python, Perl) for automation, modeling, and data-driven decision making.
- Clear technical communication and the ability to document and defend engineering decisions.
Preferred Qualifications:
- Proven record of improving real product performance through system–manufacturing co-optimization.
- Evidence of fast abstraction, strong pattern recognition, and deep systems thinking.
- Proof of work: architectures you’ve designed, performance problems you’ve untangled, or complex trade-offs you’ve driven to closure.
- Ability to operate independently on hard, ambiguous problems,and collaborate clearly across functions.
- Thoughtful use of AI to increase engineering velocity without lowering the technical bar.