Google DeepMind · London
Research Engineering Manager, AI for Chip Design
28.10.2025
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.