AI Developer — Interconnect Hardware Frontend

NVIDIA is seeking a strong hardware engineer to drive AI adoption for the MSS-Interconnect frontend team. In this role, you will identify where modern AI can create real value for RTL, verification, debug, and design-review workflows, and turn promising capabilities into practical solutions engineers use every day. You will work across global, cross-site RTL, verification, CAD, and methodology teams to improve productivity through trusted, scalable AI workflows.

Identify high-impact opportunities to apply AI across RTL, verification, debug, code understanding, and design-review workflows. Continuously evaluate new AI tools, models, and agent capabilities, and determine which are worth adopting for real engineering work. Build and maintain AI-assisted workflows, tools, and reusable components that improve team productivity. Partner with hardware engineers to turn real pain points into practical AI use cases and iterate based on usage and feedback. Drive adoption beyond early prototypes by improving workflow quality, reliability, and long-term usefulness. Help the team make sound decisions on where to experiment, invest, and scale as the AI landscape evolves. What we need to see: BS or MS in Electrical Engineering, Computer Engineering, or a related field, or equivalent experience. 3+ years of relevant experience in ASIC / SoC frontend engineering, verification, design methodology, or engineering productivity tooling. Strong understanding of hardware frontend workflows, including RTL design, verification, debug, and design reviews. Strong Python and software engineering skills, with experience building practical automation or tools for engineers. Sufficient hardware depth to judge whether an AI-assisted solution is useful, technically sound, and deployable. Strong problem-solving, communication, and cross-team collaboration skills. Ways to stand out from the crowd: Experience building or deploying LLM-based tools, agents, or AI-assisted workflows for engineering users. Strong hands-on familiarity with modern AI tooling and good judgment on which new tools are worth trialing or adopting. Experience driving sustained adoption of internal tools, not just prototypes or isolated evaluations. Familiarity with frontend hardware development environments and debug-intensive workflows. Background with Interconnect, NoC, Memory System, bus-fabric, or related silicon domains.