About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Fundamentally, physical reality is quantum, but all current computers are classical. Because of this mismatch, we cannot efficiently simulate the fundamental level of our physical reality.

Our mission is to develop this missing capability and unlock solutions to classically intractable problems. To build practical quantum computers, we must overcome noise and decoherence. We do this by instrumenting quantum programs with intermediate measurements that give us diagnostic information about errors taking place in the quantum processor in real-time as it executes quantum algorithms. This technology is known as quantum error correction (QEC) and is a key component of Google's quantum computing roadmap.

QEC Software team develops classical software necessary to achieve quantum fault-tolerance, including compilers for instrumented quantum circuits and syndrome decoders for real-time error diagnostics.

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.

The US base salary range for this full-time position is $147,000-$211,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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.

Responsibilities

• Develop software system for real-time diagnostics of errors that inevitably occur in quantum computations due to noise and decoherence using syndrome information from the quantum computer readout electronics.

• Develop software framework for performance benchmarking of the above system for real-time error diagnostics.

• Integrate quantum error correction software with quantum computer's control system.

• Gather software requirements by collaborating with researchers on error correction experiments and simulations.

• Help implement good software engineering practices to ensure robust code health of QEC software.

Minimum qualifications

• Bachelor's degree in Computer Science, Mathematics, Physics, a related field, or equivalent practical experience.

• 3 years of experience with C++ and Python.

• Experience designing and developing complex software systems.

Preferred qualifications

• Experience in systems programming, High-Performance Computing (HPC), Real-Time Computing (RTC), device driver development, kernel development, concurrency, or low-level code optimization.

• Knowledge of quantum computing, quantum error correction, classical coding theory, linear algebra, or statistics.

• Familiarity with FPGA, DSP or GPU programming.

• Familiarity with graph algorithms.