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Snapshot
At Google DeepMind, you’ll have the opportunity to revolutionise AI by applying state-of-the-art AI to Chip Design. We develop research breakthroughs that impact Google's products and services. We will be enabling the most advanced AI models running on the most advanced chips.
About Us
We develop and apply state-of-the-art AI methods and models to Chip Design and work closely with research and product teams across Google.
Our team is composed of research scientists, research engineers and software engineers that have already had a big impact on real products via research breakthroughs. We work on lighting the path of new ideas that can become new products. We work closely with hardware engineers, architects so we can bring novel ideas to real products.
We have recently participated and won the IWLS 2023 Programming Contest by applying Deep Learning, Simulated Annealing and Reinforcement Learning to logic synthesis. Our AI floorplanning tool, AlphaChip, was used to design several generations including the latest Trillium of Google's TPUs.
The mission of our team is to “Explore new spaces and bring back the learnings to deliver breakthroughs.” At Google DeepMind we've built a unique culture and work environment where long-term ambitious research grounded in real problems can flourish.
The Role
As part of our team at Google DeepMind you'll have opportunities to advance AI for Chip Design to enable breakthrough capabilities, and pioneer next-generation products in collaboration with major product teams.
There are many fundamental research and transformative product landing opportunities, including but not limited to:
- Bring the most advanced ML models and technologies to Chip Design.
- Develop breakthroughs that will have a big impact for Google and for the whole Chip design industry.
- Use LLMs and transformer models to accelerate chip design.
- Solve some of the most complex tasks in Chip Design (RTL generation, RTL verification, Logic Synthesis, Physical Design, PPA prediction, …).
Key responsibilities:
- Contribute and drive ML for Physical Design, Logical Synthesis, Verification and RTL generation.
- Architect, guide and vet designs, algorithms and solutions, writing development code to solve ambiguous problems.
- Identify unsolved impactful research problems in Chip Design, inspired by current and future real world needs.
- Define Scope, plan and organise projects towards common goals.
- Amplify impact by generalising solutions into reusable libraries for many use cases. Share knowledge through publications, open sourcing and education.
About You
In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:
- Ph.D. in Computer Science or related quantitative field, or B.S./M.S. in Computer Science or related quantitative field with 5+ years of relevant experience.
- Experience in Machine Learning (ML), especially on ML for hardware, ML for compilers or ML for optimization.
In addition, any of the following background would be an asset:
- Experience or interest in Chip & Hardware Design, especially on automating Chip Design including EDA.
- Experience with JAX, TensorFlow, PyTorch or similar. Developed and maintained Machine Learning Infrastructure
- Self-directed engineer/research scientist who can drive new research ideas from conception, experimentation, to productionisation in a rapidly shifting landscape. Excel at teamwork and cross-team collaborations.
- Strong research experience and publications in Machine Learning, Differentiable Programming, Discrete Optimization, Reinforcement Learning, Chip & Hardware Design or related fields.
