Research Engineer, Chip Verification

Snapshot

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.

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 all aspects of the Electronic Design Automation (EDA) process, to significantly reduce the time to market of Google’s products and services.

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, and ML model developers to generate novel ideas and bring them to  products. 

We are core contributors to MLIR, which we use as a core technology to represent abstractions from models, to transformations, hardware and simulators. The mission of our team is to enable “near infinite compute at near zero cost”. This implies working across the entire software and hardware stack to discover opportunities for optimization and create AI based-technologies that improve the efficiency of training and serving AI workloads.

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 teams spanning 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 breakthrough technologies that will have a big impact for Google products and for the whole chip design industry.
• Enable efficiency across the entire AI learning stack.
• Solve some of the most complex tasks in automating chip design: hardware-software co-design for ML models, hardware generation and verification, RTL optimization, and system design.
• Engage and work in a fast paced, rapidly shifting environment, demonstrating flexibility and the ability to bring clarity in ambiguous problem spaces.

Key responsibilities:

• Contribute and drive ML for hardware-software co-design.
• Contribute and drive hardware generation technologies, using ML techniques, traditional compiler-based transformations, and novel program generation techniques, such as program synthesis and constraint-based programming.
• Identify unsolved impactful research problems in chip design, inspired by current and future real world needs.
• Collaborate with model developers, software developers, and hardware architects to ensure generated designs are correct and efficient.
• Amplify impact by generalising solutions in open-source code, publications 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 code-generation technologies: intermediate representations, program synthesis, programming with constraints.
• Experience in Machine Learning (ML), especially on ML for hardware, ML for compilers or ML for optimization.

In addition, the following would be an advantage: 

• Experience with MLIR, and in particular dialects specialized for hardware synthesis, e.g., CIRCT, XLS, and similar.
• Experience with JAX, TensorFlow, PyTorch or similar. 
• Experience or interest in hardware verification technologies, formal methods, theorem proving, and hardware testing methodologies.
• 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 relevant fields: hardware synthesis and verification, machine learning, or code-generation.

 

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.