Senior RTL Optimization Engineer — EDA Tools

Google DeepMind

Zürich

Vor Ort

CHF 120.000 - 190.000

Vollzeit

Vor 5 Tagen
Sei unter den ersten Bewerbenden
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Zusammenfassung

Google DeepMind is seeking a software engineer to help design, develop, and optimize software tools for RTL generation and hardware compilation within our AI/EDA pipeline. You will collaborate with machine learning researchers, hardware architects, and software engineers to push performance and reliability.

You will implement robust automation, profile bottlenecks, and contribute to production-grade tools used across chip design and simulation workflows.

Qualifikationen

  • Bachelor's degree or equivalent practical experience.
  • 8 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript).
  • 5 years of software development experience, and with data structures/algorithms.
  • Preferred: Master's degree or PhD in Computer Science or related technical field.

Aufgaben

  • Design, develop, and optimize software tools to improve RTL quality and accelerate hardware compilation and EDA workflows.
  • Profile, identify, and resolve performance bottlenecks in software tools to speed up optimization and synthesis cycles.
  • Collaborate with hardware architects, compiler engineers, and ML researchers to bridge high-level representations with optimized RTL.
  • Develop robust automation scripts and libraries to streamline RTL generation and optimization.
  • Work with product teams to bring tools to production quality.

Kenntnisse

Software development
Data structures & algorithms
Programming languages

Ausbildung

Bachelor's degree or equivalent practical experience
Master's degree or PhD in Computer Science or related field

Tools

Hardware design
Hardware EDA tools

Jobbeschreibung

Google DeepMind is seeking a software engineer to help design, develop, and optimize software tools for RTL generation and hardware compilation within our AI/EDA pipeline. You will collaborate with machine learning researchers, hardware architects, and software engineers to push performance and reliability.

You will implement robust automation, profile bottlenecks, and contribute to production-grade tools used across chip design and simulation workflows.

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