ASIC Engineer, Physical Design CAD

Meta

Bengaluru

On-site

INR 3,500,000 - 5,500,000

Full time

6 days ago
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Job summary

Meta is seeking a Physical Design CAD Engineer to own and optimize the digital physical design flow across synthesis, floorplanning, placement, routing, and timing signoff. You will automate flows with Python and Tcl, integrate with build and CI systems, and work with PD engineers to resolve QoR issues on active tapeouts.

You will qualify EDA tools, manage tool version migrations, and contribute to flow validation, metrics, and documentation.

Qualifications

  • Bachelor’s or Master’s degree in Electrical or Electronics Engineering, Computer Engineering, and/or equivalent practical experience.
  • 2+ years of experience in ASIC physical design or CAD with automation/flow development.
  • Experience with digital physical design implementation on advanced process nodes (synthesis, place & route, timing closure).
  • Hands-on experience with industry physical design tools such as Fusion Compiler, Genus, Innovus, PrimeTime, Tempus, or StarRC.
  • Proficiency in Tcl and Python for EDA flow development and automation.
  • Experience debugging flow, timing, or physical verification issues on production tapeouts.
  • Experience with version control and working across distributed teams.
  • Experience with power integrity/EM/IR analysis and physical verification.

Responsibilities

  • Develop, maintain, and own stages of the digital physical design flow — synthesis, floorplanning and design planning, placement, CTS, routing, extraction, timing ECO, and STA signoff
  • Build and extend flow automation in Python and Tcl, integrated with Meta’s internal build, job-scheduling, and CI infrastructure
  • Collaborate with PD engineers to debug flow and QoR issues on active tapeouts, and convert recurring workarounds into features
  • Qualify and integrate commercial EDA tools, drive tool migrations with vendor teams
  • Build constraint and netlist validation to catch design-intent errors in-flow
  • Develop QoR tracking, metrics, and dashboards for convergence visibility
  • Write and maintain user-facing flow documentation and training, on-call rotation support
  • Collaborate with external design partners and vendors on flow alignment and handoff criteria

Skills

Python scripting
Tcl scripting
Automation development
QoR analysis
Cross-team collaboration
Flow documentation

Education

Bachelor’s or Master’s in Electrical/Electronics or Computer Engineering

Tools

Fusion Compiler
Genus
Innovus
PrimeTime
Tempus
StarRC

Job description

About

Meta's silicon engineering team is building custom infrastructure silicon to power the compute demands of AI, data center, and next-generation hardware platforms.The Infra Silicon Implementation CAD team owns the physical design flows and automation that take our designs from RTL handoff through synthesis, place and route, timing closure, and physical signoff. As a Physical Design CAD engineer, you will be responsible for building and owning the flow infrastructure used daily by PD engineers across concurrent tapeout programs. This is a high-leverage role: the automation you build directly determines how quickly a chip organization converges.

Responsibilities
  • Develop, maintain, and own stages of the digital physical design flow — synthesis, floorplanning and design planning, placement, CTS, routing, extraction, timing ECO, and STA signoff
  • Build and extend flow automation in Python and Tcl, integrated with Meta’s internal build, job-scheduling, and continuous integration infrastructure
  • Partner with PD engineers and design leads to debug flow and Quality of Release (QoR) issues on active tapeout programs, and convert recurring workarounds into supported flow features
  • Qualify and integrate commercial EDA tools, drive issues to resolution with vendor application-engineering teams, and manage tool version migrations across programs
  • Build constraint and netlist validation capability so design-intent errors are caught in-flow rather than at sign-off
  • Develop QoR tracking, metrics, and dashboards that give design teams and program leadership visibility into convergence across blocks and subsystems
  • Write and maintain user-facing flow documentation and training, and support designers as part of a shared on-call rotation
  • Collaborate with external design partners and foundry or ASIC vendors on flow alignment and design handoff criteria
Minimum Qualifications
  • Bachelor’s or Master’s degree in Electrical or Electronics Engineering, Computer Engineering, and/or equivalent practical experience
  • 2+ years of experience in either ASIC physical design or CAD, including hands-on work with automation, flow development, drafting methodologies, solutions, and/or hands-on work with placement, clock tree synthesis, routing, and timing closure
  • Experience with digital physical design implementation on advanced process nodes, including synthesis, place and route, and timing closure
  • Hands-on experience with industry physical design tools, such as Fusion Compiler, Genus, Innovus, PrimeTime, Tempus, or StarRC
  • Proficiency in Tcl and Python for EDA flow development and automation
  • Experience debugging flow, timing, or physical verification issues on a production tapeout
  • Experience with version control and collaborative code review Experience working across geographically distributed teams and with external design partners
  • Experience with power integrity and EM/IR analysis, and/or physical verification and logic equivalence sign-off
  • Experience with timing constraint development and validation methodology, including SDC generation and constraint checking
  • Experience building or owning a production CAD or physical design flow used by multiple design teams
  • Experience building data pipelines or dashboards for design metrics and QoE analysis
  • Experience with compute and license capacity management for large EDA workloads
  • Experience applying AI or LLM-based tooling to engineering workflows
  • Experience with hierarchical design planning and block-level partitioning on large SoCs
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