About the job

At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.

As a Research Engineer on our Science Team, you will contribute to our Quantum Computing Initiative. This role is an excellent opportunity for a creative and driven engineer who truly burns for the immense possibilities of AI for Science.

In this role, you will act as the crucial bridge between research and implementation, by seamlessly linking quantum computing research with ML. As an amazing team player, you will collaborate with domain experts in (quantum) computer science, mathematics, computational chemistry, physics, and machine learning.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

Responsibilities

• Deliver on complex research and engineering milestones, assuming end-to-end ownership of implementing and scaling ML solutions (traditional and agentic) at the intersection of quantum computing and machine learning.
• Complement the algorithms development pillar by translating classical and quantum algorithms into robust code, balancing implementation time with efficiency, design simplicity, and sustainability.
• Facilitate communication between research and engineering by advocating code readability, promoting engineering best practices, and writing effective design documents.