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Sr / Staff AE For Physical Implementation RTL-GDS

Sr / Staff AE For Physical Implementation RTL-GDS

ConfidentialBengaluru / Bangalore
30+ days ago
Job description

Responsibilities :

As Senior Staff Application Engineer (AE) , you will be

  • Working on latest Synopsys implementation technologies ( Machine Learning , Physical Synthesis , Multi Source CTS etc ) to solve complex PPA challenges faced by Synopsys customers.
  • Working on benchmarks to displace competition implementation solutions
  • Working with customers to develop and debug RTL-GDS implementation methodologies and flows.
  • Providing technical solutions by identifying the design and / or EDA tool issues and provide appropriate solution for customers
  • Effectively translate the findings into requirements for R&D to improve both tool behavior with enhancements as adaptive long-term solutions.
  • Involved in deployment of new technologies on latest EDA versions and enable customers to migrate to newer versions achieving best PPA
  • Coming up with proactive understanding of customers pain point and come up with innovative solutions to address the same.
  • Closely interacting with Synopsys R&D team and product development team to develop future technologies
  • This role requires you to act as customers advocate while talking to inhouse R&D, and be a product brand ambassador while engaging with customers.

Requirements :

  • At-least 10 years of experience in Physical Implementation RTL-GDS. Experience in unsupervised debugging and resolving synth & PnR implementation challenges.
  • Candidate must have good exposure towards methodology changes to achieve targeted PPA metrics for complex designs.
  • Proficiency in Synopsys implementation tools is an advantage
  • The person must be self-motivated and dedicated with solid debug skills.
  • Requires proficiency in scripting (tcl / unix / perl).
  • Excellent communication skills including ability to interface with customers and business unit personnel are essential.
  • Skills Required

    Physical Synthesis, Machine Learning

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