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Niche EDA+ML role reduces applicants despite metro, mid-level experience, and recognizable semiconductor brand.
Requires deep ASIC/EDA and ML expertise, making skills highly industry-specific and less transferable.
Mandatory 5 years plus specific Python/C++ and UNIX/Git skills create strict filtering.
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Develop and prototype AI/ML-driven automation solutions to improve efficiency and quality (power, performance, area, verification) of SoC and IP design and implementation flows.
Collaborate closely with AI/ML experts, architecture, IP, SoC design, verification, implementation, and enablement teams to evaluate and integrate state-of-the-art AI/ML techniques into VLSI CAD software and ASIC design processes.
Drive digital transformation by applying AI/ML methods such as GenAI, Reinforcement Learning, Graph Neural Networks, and CNNs to enhance advanced node SoC design targeting Edge-AI, automotive processors, and microcontrollers.
Bachelor's or Master's degree in Computer Science, Electrical Engineering, Computer Engineering or related discipline.
Minimum 5 years of hands-on experience in complex software development projects with strong proficiency in Python and C++ programming.
Experience working in Unix/Linux environments with knowledge of revision control tools (Git, BitBucket) and software development/debug tools.
Strong foundation in AI/ML techniques and software development, with understanding of VLSI CAD/EDA tools as a plus but not mandatory.
Experienced software engineer with strong AI/ML expertise applied to system design automation or related fields, comfortable bridging between VLSI design and advanced AI/ML methodologies.
Practitioner-oriented mindset who can deliver prototype solutions and pilot automation workflows on real SoC and IP designs in a multi-disciplinary, multi-site engineering environment.
Skilled collaborator able to engage academic research and multiple internal teams to translate research innovations into practical CAD tool enhancements and ASIC design improvements.