We are looking for engineers who understand that the end of Moore’s Law will require a revolution in chip design methodology (EDA). Our work emphasizes three opportunities for novel approaches to EDA: cloud/hyperscale computing--which allows algorithms to operate totally differently; machine learning--which provides dramatic automation opportunities; and integration--since we can now co-design and/or co-optimize algorithms, compilers, and hardware. This is a hands-on coding role for engineers who like to roll up their sleeves and get things done. At Google DeepMind you’ll be joining our team of scientists and engineers who are directly impacting the machinery of machine learning. You should be passionate about making digital electronic design better, including (but not limited to) how to use machine learning, cloud computing, and/or co-design to dramatically improve EDA: Skilled at rapid yet rigorous prototyping and software tool building for design automation; aptitude to quickly determine whether an approach is worth following or not without spending too much time. Excited to invent, prototype, and deliver tools employing novel optimization techniques and machine learning to re-invent AI chip design, from RTL to GDSii. Existing team members are self-driven and motivated, bring passion to their work, are comfortable working in a highly ambiguous environment, are open and curious to seek out and learn new perspectives, can challenge their own thinking, and are excellent and direct communicators and collaborators.
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Job Type
Full-time
Career Level
Mid Level
Education Level
Ph.D. or professional degree