Staff/Senior Staff Physical Design Floorplan & PDN Lead
EnCharge AI
Salary undisclosed
Need a reasonable accommodation to apply or interview? Contact us.
JobMinglr uses automated technology to recommend jobs based on profile information and job preferences. Match Score does not determine eligibility for a position, prevent a user from viewing or applying to a job, or make hiring decisions on behalf of an employer.
Description
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.
Staff/Senior Staff Physical Design Floorplan & PDN Lead
Experience: 10–14 Years
Location: Bangalore/2 days to work
Department: RTL-to-GDSII / Physical Implementation
Role Overview
We are seeking a high-caliber Staff or Senior Staff Physical Design Engineer with a specialized mastery of Floorplanning and Power Delivery Network (PDN) design. You won’t just be pushing buttons; you will be the architectural bridge between RTL/Systems and the final GDSII. This role requires a visionary who understands how a single floorplan decision ripples through the entire PPA (Power, Performance, Area) spectrum.
As a technical lead, you will own the chip-top floorplan for complex, large-scale SoCs or Sub-chips, driving strategies that balance aggressive performance targets with robust power integrity.
Key Responsibilities
- Architectural Floorplanning: Lead chip-level floorplanning, including macro placement, pin assignment, and partition definition for multi-million gate designs.
- Strategic PDN Design: Architect and implement complex Power Delivery Networks. You will define the metal stack usage and grid density to support high-performance cores while minimizing routing congestion.
- EMIR Mastery: Perform exhaustive IR-drop (Static/Dynamic) and Electromigration (EM) analysis. You must be able to diagnose root causes and propose architectural or physical fixes that don't compromise timing.
- Cross-Functional PPA Trade-offs: Collaborate directly with RTL and Architecture teams. You will use "Design Thinking" to influence the micro-architecture, suggesting changes to bus widths, pipeline stages, or memory configurations to optimize physical outcomes.
- Methodology Development: Establish best practices and automated flows for floorplanning and PDN synthesis to be used by the wider implementation team.
Technical Requirements
- Core Experience: 10+ years in Physical Design with a proven track record of multiple successful tape-outs at advanced nodes (7nm, 5nm, or 3nm).
- PDN Expertise:Ability to model complex power-up sequences and multi-voltage domains.
- Floorplanning Savvy: Deep proficiency in hierarchical physical design and top-level integration using industry-standard tools (Innovus or IC Compiler II).
- Analysis Skills: Strong understanding of thermal-aware PDN and the physical implications of high-current density paths.
- Scripting: Advanced proficiency in Tcl, Python, or Perl to automate complex floorplanning tasks and data analysis.
Behavioral Attributes
- Critical Thinker: You don't just follow a recipe. You question constraints and look for "hidden" PPA gains.
- Design Thinking: Ability to empathize with the challenges of upstream (RTL) and downstream (Sign-off) teams to create a holistic solution.
- Influence: You can articulate technical trade-offs to stakeholders,
Education
- B.Tech/M.Tech in Electrical/Electronic Engineering or equivalent.
About this role
EnCharge AI is building next-generation AI hardware for edge computing, and this role sits at the technical heart of that effort. You would own the floorplanning and power delivery architecture for complex, large-scale chips—making decisions that directly shape power efficiency, performance, and area across the entire design. This isn't a hands-on execution role; it's a leadership position where your architectural choices ripple through every downstream team and ultimately determine whether the chip meets its aggressive performance and power targets.
The work demands 10+ years of physical design experience at advanced nodes (7nm and below), with deep expertise in PDN modeling, hierarchical floorplanning, and EMIR analysis. You'll need to be equally comfortable with industry tools like Innovus, scripting in Tcl or Python to automate complex tasks, and—critically—collaborating with RTL and architecture teams to influence micro-architectural decisions before they become locked into silicon. The role calls for someone who questions constraints, diagnoses root causes under pressure, and can articulate technical trade-offs clearly to stakeholders.
This suits a senior engineer who has shipped multiple chips at advanced nodes and is ready to move into a technical leadership role where influence and systems thinking matter as much as deep technical execution. The position is in-office in Bengaluru with a hybrid arrangement (2 days on-site), and requires a B.Tech or M.Tech in electrical or electronic engineering.
How this employer is doing
average
- H1B: Sponsors Visas
- Sponsors Visas
This role's local market on JobMinglr
Pay for this role
The employer didn't post a pay range for this role. That usually means pay is set in negotiation, which favors whoever arrives with numbers. Check ranges on comparable Staff/Senior Staff Physical Design Floorplan & PDN Lead postings in Bengaluru, and analyze any offer before you accept it.
Before you apply, worth reading
How JobMinglr reads this job
Every listing here is scored against your profile before you apply: skills overlap, experience level, location and work arrangement, each weighted and explained. You see the score and the reasons, not just a list. How the matching works.