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VLSI Physical Design Engineer

Hailo
July 21, 2026
Full-time
On-site
Springfield, Illinois, United States
Physical Design Jobs, Level - Mid-Career

Job Title

VLSI Physical Design Engineer

Role Summary

Join the VLSI group to implement and optimize physical design flows for Hailo's edge AI processors. The role covers full-cycle physical design activities and development of back-end (BE) flows that enable tape-outs.

The engineer will collaborate with RTL, verification, architecture and software teams to deliver high-performance designs for advanced process nodes.

Experience Level

Mid-level β€” typically requires 3+ years of VLSI physical design experience.

Responsibilities

The role involves participating in all phases of physical design and contributing to automation and signoff flows.

  • Perform synthesis, implementation, place & route and timing closure (STA).
  • Optimize performance, power and area (PPA) across design stages.
  • Develop and maintain BE flows, scripts and RTL-to-GDSII flows.
  • Execute signoff verification flows and debug CAD/tool issues.
  • Address constraints and requirements for advanced process nodes.
  • Coordinate with RTL, verification and architecture teams to meet tape-out schedules.

Requirements

Key must-have skills and a few desirable extras.

  • Must-have: 3+ years as a VLSI physical design engineer.
  • Deep practical knowledge of physical design methodologies: synthesis, implementation/place & route, STA, and PPA optimization.
  • Hands-on experience with CAD tools, RTL-to-GDSII flows and signoff verification flows.
  • Experience working with advanced process nodes.
  • Nice-to-have: experience developing BE flows and automation scripts; experience with low-power design techniques.

Education Requirements

B.Sc. or M.Sc. in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field from a recognized university.


About the Company

Company: Hailo

Headquarters: Tel Aviv, Israel

Hailo develops high-performance AI processors and software for edge devices, enabling efficient on-device neural network inference across industries such as automotive, smart cities, manufacturing, agriculture, and retail.

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Date Posted: 2026-07-21