Google DeepMind · London
Research Engineering Manager, AI for Chip Design
10/28/2025
Description
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.
Qualifications
- 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.
- Experience or interest in Chip & Hardware Design, especially on automating Chip Design including EDA.
- Self-directed research engineer who can land new research ideas into 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.