Join Google's pioneering team as a Software Engineer focused on Persistent Disk technology. This role offers a unique opportunity to shape the future of large-scale information systems, impacting billions of users globally. We seek innovative minds ready to tackle complex challenges in areas such as information retrieval, distributed computing, system design, and data storage.

This position is part of the AI and Infrastructure (AI2) organization within Google Cloud, a dynamic group responsible for the core software, hardware, and Machine Learning systems that power essential Google services and Cloud products. As a PhD Software Engineer, your advanced research expertise will be vital in addressing real-world problems at an unprecedented scale.

As a key contributor, you will be instrumental in designing, testing, deploying, and maintaining advanced software solutions. Your responsibilities will include driving research initiatives to explore next-generation storage architectures, prototyping novel backup and replication algorithms, and evaluating emerging technologies. You will also focus on enhancing the reliability, scale, and performance of our backup and replication solutions while managing healthy release cycles. Collaboration with internal stakeholders and partners will be crucial to translate complex engineering and research efforts into improved customer satisfaction and business growth.

This role requires a PhD degree in Computer Science, Electrical Engineering, or a related technical field. Candidates should possess experience in the research and development of data storage, backup, or disaster recovery technologies, including conducting research on storage systems and publishing findings. Proven experience in the architecture or development of distributed systems, encompassing concurrency, multi-threading, or synchronization, is essential. Proficiency in programming languages such as C, C++, Python, Java, or Golang is required. Experience with data management tools, research community contributions, and performance analysis is highly preferred.