Staff Software Engineer, Google Cloud CISO at Google in Sunnyvale, CA, USA

Job description

• Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. 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 Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. 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.
• Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
• US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
• Learn more about benefits at Google.

Responsibilities

• Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.
• Define the multi-year technical vision and architectural roadmap for high-efficiency, formally verified systems across Google Cloud CISO.
• Lead the end-to-end architecture and implementation of distributed transactional systems, storage layers, and query engines built in Rust and verified with Verus. Formulate verification strategies for complex, multi-component distributed protocols, proving correctness, fault tolerance, and data integrity under arbitrary failure modes.
• Lead initiatives to replace bug-prone, resource-heavy legacy components with formally verified, resource-efficient alternatives across internal and open-source infrastructure. Partner with VP-level and Principal engineering leadership to establish organization-wide verification standards, security baselines, and developer tooling.

Requirements

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience architecting, implementing, and operating large-scale distributed systems, transactional storage engines, or high-performance database internals.
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience developing production-critical systems in Rust.
• 3 years of experience in technical leadership, setting multi-year architectural roadmaps, and driving cross-organizational technical initiatives.
• Experience with formal specification, formal methods, or verified systems development (e.g., Verus, TLA+, SMT verification, or proof-carrying code).
• PhD in Computer Science with a focus on Distributed Systems, Transactional Storage, Database Engines, or Formal Verification.
• Track record of architecting petabyte-scale storage engines, distributed consensus protocols or low-latency key-value databases.
• Expertise in hardware-conscious algorithmic design, query optimization, LSM-tree architectures, and zero-copy memory pipelines.
• Contributions to the broader systems and Rust open-source ecosystems (e.g., authoring/maintaining widely adopted crates, database engines, or storage primitives).
• Ability to influence executive leadership and drive adoption of transformative security and verification technologies across multiple product areas.
• Research publications in systems and data engineering venues (e.g., OSDI, SOSP, NSDI, VLDB, SIGCOMM, EuroSys).

Skills

Required

• Transactional storage engines
• High-performance database internals
• ML design and ML infrastructure
• Technical leadership

Preferred

• Distributed consensus protocols
• Low-latency key-value databases
• Hardware-conscious algorithmic design
• Query optimization
• LSM-tree architectures
• Zero-copy memory pipelines
• Influencing executive leadership