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Staff R&D Engineer (Machine Learning)

Synopsys

Synopsys

Software Engineering
Bengaluru, Karnataka, India
Posted on Feb 4, 2025

Job Overview

Pattern Analytics is a new field at the intersection of Electronic Design Automation (EDA), Data Science and Machine Learning, applied to Semiconductor Design and Silicon Manufacturing flows.

The role of Pattern Analytics Staff Engineer includes an understanding of the state of the art in EDA, and strong technical creativity in the identification of existing open problems and critical gaps in current industrial solutions.

Technical skills will need to be paired with creative approaches in the development of computational solutions, from prototype to full product implementation. Thus, the successful Pattern Analytics Staff Engineer will work with a multi-disciplinary team to build and test new families of EDA products.

Responsibilities and Duties

  • Work with a cross-functional, multi-disciplinary team to identify new methodological approaches to layout analysis and define algorithmic solutions
  • Develop software modules and shared libraries throughout a product life cycle, from prototype to customer release
  • Implement testing and benchmarking automation for Pattern Analytics tools
  • Coordinate the creation of engineering-level technical documentation for the supported products
  • Troubleshoot and solve customer problems after deployment

Qualifications

  • Ph.D. or BTech/MTech (with 3+ years' industry experience) in EE or CS or related fields.
  • Must have skills:-
    • Practical experience in developing Machine Learning based tools/applications using Generative AI, CNN etc.
    • Programming Languages:
      • Python 3, Javascript
      • C++ or Java
  • Good to have skills:-
    • Experience in Electronic Design Automation, and/or VLSI Physical Design Verification, and/or Mask Data Generation is a plus.
    • Probability and Statistics
    • Programming
      • Asynchronous programming
      • Applications/Libraries for Data Visualization
      • Application/Libraries for Computational Geometry and VLSI Layout manipulation