Staff Software Engineer, Quantum Error Correction Decoding, Quantum AI

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.

With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

On its fundamental level, our physical reality is quantum, but our most powerful computers are classical. As a result, we cannot efficiently simulate the fundamental level of our physical reality -- a major roadblock for a technological civilization. Our mission at Google Quantum AI is to develop this missing capability and unlock solutions to classically intractable problems. This entails developing a practical implementation of quantum error correction (QEC) to overcome noise and decoherence.

The QEC Software team is developing classical software necessary to achieve scalable quantum computing, including compilers for fault-tolerant quantum circuits and syndrome decoders for real-time error diagnostics.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Propose, implement, and evaluate novel QEC decoding algorithms (e.g., for quantum LDPC codes targeting superconducting qubit and neutral atom architectures), automating evaluation pipelines to facilitate AI-assisted research.
• Architect and define scalable APIs to seamlessly integrate novel syndrome decoders and diagnostic tools with the broader classical software and quantum hardware stack.
• Partner cross-functionally with research scientists and electronics engineers to gather requirements and directly support live QEC experiments on Google's quantum computers.
• Drive technical strategy, scoping, and roadmap prioritization for complex quantum software initiatives across fault-tolerant computing workflows.
• Advocate engineering best practices across the team and actively mentor junior engineers through design reviews and technical guidance.

Qualifications

Minimum qualifications:

• Bachelor's degree in Computer Science, Electrical Engineering, Physics, Applied Mathematics, or equivalent practical experience.
• 5 years of software development experience using C++ or Python, with experience in data structures and computer science.
• Experience designing, implementing, and optimizing novel algorithmic solutions for computational or performance-critical problems.
• Experience in systems-level programming, code optimization, or high-performance computing (e.g., concurrency, multithreading, or GPU acceleration).
• Experience leading technical design, defining system architectures/APIs, and mentoring junior engineers.

Preferred qualifications:

• Master's degree or PhD in Physics, Computer Science, Mathematics, or a related quantitative field.
• Experience developing or benchmarking syndrome decoding algorithms (e.g., BP-OSD, MWPM, tensor networks, or neural network decoders).
• Expertise in quantum error correction (QEC), including surface codes, color codes, or quantum LDPC (qLDPC) codes.
• Familiarity with physical qubit platforms and architectures, particularly superconducting qubits, neutral atoms, or trapped-ion systems.
• Strong background in classical coding theory, graph algorithms, belief propagation, or probabilistic modeling.