Research Engineer, Chip Design

Key Skills

ChIP-Seq, JAX framework, Large Language Models - LLMs, Machine learning techniques, PyTorch, Reinforcement Learning, RTL design, TensorFlow

Job Description

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create outstanding impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

This role can be based in Mountain View, San Francisco, London, or Paris.

Snapshot

At Google DeepMind, you’ll have the opportunity to revolutionise AI by applying powerful AI to Chip Design. We develop research breakthroughs that impact the different products at Google. We will be enabling the most sophisticated 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 collaborating with research and product teams across Google. Our teams mission is to “Explore new spaces and bring back the learnings to deliver breakthroughs.”

Our team is composed of researchers, 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 recently participated in and won the IWLS 2023 Programming Contest by generating new circuits from scratch using Deep Learning, Simulated Annealing and Reinforcement Learning.

At GDM, we've built an outstanding culture and work environment where long-term ambitious research grounded in real problems can flourish.

The role

As part of our team, you'll have opportunities to advance AI for Chip Design research to enable breakthrough capabilities, and pioneer next-generation products in collaboration with major Product Areas.

There are many fundamental research and transformative product landing opportunities, including but not limited to:

• Bring sophisticated ML models and technologies to Chip Design.
• Develop breakthroughs that will have significant impact for Google, and for the whole Chip design industry!
• Use LLMs and transformer models to accelerate chip design.
• Tackle some of the most complex tasks in Chip Design (verification, RTL generation, PPA modelling, …)

Key responsibilities:

• Contribute and drive ML for 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 others 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 in this role, we look for the following skills and experience:

• PhD in Computer Science or related quantitative field, or B.S./M.S. with 5+ years of relevant experience.
• Experience in ML, especially ML for hardware, ML for compilers or ML for optimization.

In addition, the following would be an advantage:

• Experience or interest in Chip & Hardware Design, especially on automating Chip Design.
• 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, Reinforcement Learning, Chip & Hardware Design or related fields.