Senior Research Scientist, Quantum Demonstration Workflows, Quantum AI
Confirmed on the employer's own hiring board on Oct 26, 2026. First seen by Alion on Oct 19, 2026. Google scores B on the Alion truth index.
About the job
As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.
As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
As a Research Scientist, you will focus on pioneering the techniques, interfaces, and technology stack required to run complex quantum applications, including quantum error mitigation, on Google’s quantum processors.
Bridging theoretical protocols and physical execution, you will lead the pathfinding and development of robust workflow toolsets powering our flagship experiments. You will have a blend of deep physical insight, quantum expertise, and collaborative software engineering to integrate innovative methods into core workflows, enabling users to push the boundaries of quantum computing.
The full potential of quantum computing will be unlocked with a large-scale computer capable of complex, error-corrected computations. Google Quantum AI's mission is to build this computer and unlock solutions to classically intractable problems. Our roadmap is focused on advancing the capabilities of quantum computing and enabling meaningful applications.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Drive the prototyping and pathfinding efforts for next-generation, reconfigurable quantum workflows in quantum computing experiments on processors, and how computations are done on a real, noisy quantum processor.
- Own the end-to-end mission from initial concept to validated experimental results.
- Partner closely with Theory, Experiment, and Software Engineering teams to integrate complex quantum systems into a cohesive application stack, including components like dynamical decoupling and quantum error mitigation.
- Influence the long-term technical roadmap for quantum experiments, ensuring research breakthroughs translate into validated and scalable workflow tools.
- Facilitate the transition of research prototypes to mature useful workflow tools.
Qualifications
Minimum qualifications:
- PhD in Physics, Quantum Information, Computer Science, Engineering, or a related technical field, or equivalent practical experience.
- 3 years of experience in scientific computing and the theoretical or numerical modeling of quantum systems.
- Experience in software development and prototyping for quantum hardware (e.g., using Cirq, Qiskit, or similar frameworks).
Preferred qualifications:
- Experience contributing to or leading high-impact projects within large-scale industry or scientific collaborations.
- Experience in managing large-scale experimental data, including complex circuit generation sweeps and job execution management.
- Familiarity with quantum error mitigation, characterization methods, or hardware-aware optimization techniques.
- Excellent communication skills with the ability to bridge the gap between theoretical research and experimental execution.
- Proven track record in rapid prototyping and successfully transferring research technology to production-grade software teams.