Physical Design Engineer

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.

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  End-to-End Ownership: Drive RTL-to-GDSII implementation, including floorplanning, placement, CTS, routing, and physical verification (LVS/DRC/ERC).
  
  


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  Performance Optimization: Execute high-performance design closure focusing on PPA (Power, Performance, Area) targets in advanced process nodes ( 3nm or below)
  
  


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  Static Timing Analysis (STA): Perform comprehensive timing closure, including signal integrity, crosstalk analysis, and CPPR (Common Path Pessimism Removal) optimization.
  
  


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  Power Integrity: Conduct IR-drop analysis (Static/Dynamic) and EM (Electromigration) checks to ensure robust power delivery.
  
  


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  Innovation: Develop and integrate AI/ML-based automation scripts to optimize the physical design flow and reduce turnaround time.
  
  

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  Education: B.Tech/M.Tech in Electrical/Electronics Engineering from a Top Tier University.
  
  


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  Experience: 2–4 years of hands-on experience in Physical Design within the semiconductor industry.
  
  


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  Tool Expertise: Expert-level proficiency in industry-standard tools (Preferred Innovus).
  
  


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  Technical Depth: Strong understanding of multi-corner multi-mode (MCMM) closure, low-power design techniques (UPF/CPF), and physical verification.
  
  


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  Mindset: A proven "go-getter" who is diligent, detail-oriented, and capable of taking complete ownership of blocks under tight deadlines.
  
  


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  The "Plus": Proficiency in Python/Tcl and experience using AI/ML models for EDA tool automation or predictive PPA analysis.
  
  


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  Location: Bangalore