Team Lead, Research Engineering, AI for Chip Design in City of London

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

We develop and apply state-of-the-art AI methods and models to accelerate 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 and architects so we can bring novel ideas to real products.

The mission of our team is to “Accelerate and Optimize Chip Design with AI.” At Google DeepMind we've built a unique culture and work environment where long-term ambitious research grounded in real problems can flourish.

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 Areas. 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 ML 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:

• Manage a thriving and growing team.
• Drive the development of infrastructure for AI for Chip Design.
• Develop agentic flows, tool use and new capabilities for Chip Design.
• Contribute to ML for Physical Design, Logical Synthesis, Verification and RTL generation.
• Work closely with other REs and collaborators to deliver AI solutions to chips.
• Amplify impact by generalising solutions into reusable libraries for many use cases.

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.
• Strong coding and ML infrastructure, distributed systems, tools integrations.
• Strong hands‑on implementation experience with Deep Learning (DL) models and frameworks like PyTorch, JAX, TensorFlow, or similar.
• Publicly available evidence of DL implementation skills via open source repositories will be valuable.

In addition, the following would be an advantage:

• Experience or interest in Chip & Hardware Design, especially on automating Chip Design including EDA.
• Self‑directed research engineer who can land new research ideas in productionisation in a rapidly shifting landscape.
• Excel at leading high performing teams and cross‑team collaborations.
• Knowledge of Machine Learning, Differentiable Programming, Discrete Optimization, Reinforcement Learning, Chip & Hardware Design or related fields.

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary 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.