Superconducting Device Modeling Research Scientist, Quantum AI

Minimum qualifications

• PhD degree in quantum information related field, Physics, Electrical Engineering, Computer Science, a related field, or equivalent practical experience.
• Experience with theoretical quantum physics and related approximation methods
• Experience working with open quantum systems (numerical modeling/tooling, simulating quantum systems with noise, etc.)
• One or more scientific publication submission(s) for conferences, journals, or public repositories.

• Experience in software engineering best practices, including design and testing.
• Experience with modeling or simulating superconducting qubits.
• Experience in electromagnetic modeling/microwave engineering.
• Experience in high performance scientific computing C++ or other compiled languages.

As a Research Scientist, you will set up large‑scale tests and deploy ideas, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. You will contribute to the wider research community by sharing and publishing the findings with research programs at partner universities and technical institutes all over the world.

The US base salary range for this full‑time position is $133,000-$194,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job‑related skills, experience, and relevant education or training.

Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

• Develop numerical physical models to simulate Hamiltonians for superconducting qubits and other device components and gate dynamics under relevant error mechanisms such as decoherence.
• Identify approximations and develop numerical approaches to enable simulation of non‑trivial (e.g., non‑Markovian) noise sources.
• Expand existing simulation infrastructure to include new architectures and error models.
• Collaborate with experimentalists to validate numerical models and adapt them towards useful applications.