EnCharge AI
Staff / Senior Staff CAD & Methodology Engineer (Digital Implementation & Signoff)
Be an Early Applicant
Lead development and optimization of a highly automated RTL-to-GDSII and timing signoff flow. Own Innovus methodology and Tempus STA integration, reduce turnaround time with Python/Tcl automation, interface with Cadence, debug tool/database issues, and mentor PD and timing teams to ensure robust, high-performance physical implementation for AI accelerators.
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.
The Role
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.
Key Responsibilities
- Flow Architecture & Automation: Architect, develop, and maintain a highly automated, robust, and scalable physical design and signoff flow from synthesis through GDSII, optimized for AI accelerator architectures.
- Innovus Methodology: Drive advanced physical design methodologies in Cadence Innovus. Optimize recipes for floorplanning, placement, Clock Tree Synthesis (CTS), routing, and power grid implementation to squeeze out maximum PPA.
- Tempus Signoff Integration: Own the STA and timing signoff methodology using Cadence Tempus. Implement and optimize Distributed Multi-Scenario Analysis (DMSA), advanced OCV methodologies, and automated Tempus ECO flows to ensure rapid timing closure.
- Turnaround Time (TAT) Reduction: Identify inefficiencies in the daily execution of block and top-level implementation. Develop Python and Tcl-based automation, database parsers, and custom utilities to drastically reduce tool runtime and engineering debug time.
- Tool & Vendor Interface: Act as the primary technical liaison with Cadence. Drive tool evaluations, beta testing of new features, and resolution of critical tool bugs or limitations to ensure continuous flow stability.
- Mentorship & Support: Serve as the final escalation point for the PD and Timing teams regarding flow-related issues. Document methodologies clearly and mentor junior engineers on tool usage and flow mechanics.
Requirements & Qualifications
- Experience: 9 to 14 years of industry experience in ASIC/SOC physical design, with a primary focus on CAD, TFM (Tools, Flows, and Methodology), or flow automation.
- Cadence Mastery:
- 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.
- Scripting & Software Skills: Expert-level proficiency in Tcl and Python. Strong background in Makefile generation, shell scripting, and version control (Git/Perforce).
- Domain Knowledge: Thorough understanding of deep sub-micron physical design concepts (advanced node DRCs, cross-talk, electromigration, IR drop) and the theoretical foundations of Static Timing Analysis.
- Startup Mindset: Ability to thrive in a fast-paced, dynamic environment where flow requirements evolve rapidly as the architecture matures.
- Education: Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Computer Engineering, or a related discipline.
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