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EnCharge AI, Inc. seeks a Staff/Senior Staff CAD & Methodology Engineer to lead automated physical design and signoff flows for AI accelerators. You will be the resident expert in Innovus and Tempus, collaborating with PD and STA teams to remove bottlenecks and accelerate RTL-to-GDSII timing signoff.
The role emphasizes scalable flow architecture, Python/Tcl automation, and strong Cadence tooling expertise to meet aggressive tapeout schedules in a fast-paced startup environment.
EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.
We are an ambitious AI hardware startup building next-generation accelerators with massive compute density. To achieve aggressive Power, Performance, and Area (PPA) targets on accelerated tapeout schedules, we require a robust, highly automated, and heavily optimized physical implementation and signoff flow.
As a Staff / Senior Staff CAD & Methodology Engineer, you will be the resident expert in Innovus and Tempus, responsible for developing, debugging, and continually accelerating our RTL-to-GDSII and timing signoff flows. You will work closely with Physical Design and STA leads to eliminate bottlenecks, automate manual tasks, and push the limits of EDA capabilities at advanced process nodes.
Deep, production-proven expertise with Cadence Innovus (Implementation) and Cadence Tempus (Signoff STA).
Strong understanding of internal tool mechanics, database access commands (dbGet), and advanced tool configuration.
Education: Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Computer Engineering, or a related discipline.